Automatic Phonetic Transcription of dialectal variance in Catalan

Treball de fi de màster

Màster:

Edició:

Directors:

Any de defensa:

Col⋅lecció: Treballs de fi de màster

Programa oficial de postgrau "Comunicació lingüística i mediació multilingüe"

Departament de Traducció i Ciències del Llenguatge

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Abstract

This project, which blended two fields of linguistics, language technology and dialectology,

accomplished three main goals. Firstly, it improved TexAFon, a rule and knowledge based

speech processing tool and upgraded it to handle eight northwestern Catalan varieties.

Secondly, it enabled the tool for ASR use. Thirdly, it prepared TexAFon for use as an

automatic pronunciation training system. The dialects were chosen as a result of a thorough

research both in dialectology and language technology so as to define phenomena for Text-

To-Speech (TTS) and Automatic Speech Recognition (ASR) modeling. The result of this

project is an updated version of TexAFon, which can now be used for TTS and ASR in several

Catalan dialects, and provide the basis for its use as an automatic pronunciation training

system. Furthermore, this work also shows the upcoming need for updated dialectological

studies in order to get present information about phenomena in different Catalan dialects.

Keywords: ASR, TTS, Pronunciation Dictionaries, Automatic Phonetic Transcription, TexAFon,

dialects, Catalan.

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Acknowledgements

I would like to specially thank Juan María Garrido and Esteve Clua for guiding me along this

project. Juanma made me discover the fascinating field of language technology and Esteve

helped me in dialectology. I would also like to thank my family for being always there and

encouraging me to become the person I am today. Finally, I cannot forget to mention Becca

for her final project’s correction.

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Table of Contents

1. Introduction .................................................................................................... 6 1.1. ASR .................................................................................................................... 6 1.2. Pronunciation dictionaries .................................................................................. 8

1.2.1. Pronunciation dictionaries by corpus and rules .................................................. 9 1.3. Automatic phonetic transcription ..................................................................... 10

1.3.1. Automatic phonetic transcription in Catalan and Spanish ................................ 11 1.3.2. TexAFon ................................................................................................................ 12

1.4. Goals and methodology .................................................................................... 14

2. Definition of variants and selection of phenomena ....................................... 15 2.1. Selected inter-dialectal phenomena.................................................................. 18 2.2. Ribagorçà ......................................................................................................... 19

2.2.1. Primary phenomena .......................................................................................... 19 2.2.2. Secondary phenomena...................................................................................... 20

2.3. Pallarès ............................................................................................................ 21 2.3.1. Primary phenomena .......................................................................................... 21 2.3.2. Secondary phenomena...................................................................................... 22

2.4. Tortosí ............................................................................................................. 22 2.4.1. Primary phenomena .......................................................................................... 22 2.4.2. Secondary phenomena...................................................................................... 23

2.5. Central Area ..................................................................................................... 23 2.5.1. Primary phenomena .......................................................................................... 23

2.6. Northern Valencian .......................................................................................... 24 2.6.1. Primary phenomena .......................................................................................... 24 2.6.2. Secondary phenomena...................................................................................... 24

2.7. Central Valencian ............................................................................................. 25 2.7.1. Primary phenomena .......................................................................................... 25 2.7.2. Secondary phenomena...................................................................................... 25

2.8. Southern Valencian .......................................................................................... 26 2.8.1. Primary phenomena .......................................................................................... 26 2.8.2. Secondary phenomena...................................................................................... 26

2.9. Alacantí ........................................................................................................... 26 2.9.1. Primary phenomena .......................................................................................... 27 2.9.2. Secondary phenomena...................................................................................... 27

2.10. Pseudo-code .................................................................................................. 28

3. Implementation ............................................................................................ 28 3.1. Overview ......................................................................................................... 28 3.2. Limitations on every dialect .............................................................................. 29

3.2.1. Limitations shared in all dialects ....................................................................... 29 3.2.2. Limitations in Pallarès ....................................................................................... 30 3.2.3. Limitations in Ribagorçà .................................................................................... 31 3.2.4. Limitations in Tortosí ......................................................................................... 31 3.2.5. Limitations in Central Area ................................................................................ 32 3.2.6. Limitations in Northern Valencian ..................................................................... 32 3.2.7. Limitations in Central Valencian ........................................................................ 32 3.2.8. Limitations in Southern Valencian ..................................................................... 33 3.2.9. Limitations in Alacantí ....................................................................................... 33

3.3. Exception dictionaries ...................................................................................... 33

4. Evaluation ..................................................................................................... 34

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4.1. Evaluation of Ribagorçà .................................................................................... 35 4.2. Evaluation of Pallarès ....................................................................................... 36 4.3. Evaluation of Tortosí ........................................................................................ 36 4.4. Evaluation of Central Area ................................................................................ 36 4.5. Evaluation of Northern Valencian ..................................................................... 37 4.6. Evaluation of Central Valencian ........................................................................ 37 4.7. Evaluation of Southern Valencian ..................................................................... 37 4.8. Evaluation of Alacantí ...................................................................................... 38 4.9. Overview of the results .................................................................................... 38

5. Conclusion ..................................................................................................... 39

References ........................................................................................................... 40

Annex 1 ................................................................................................................ 44

Annex 2 ................................................................................................................ 53

Annex 3 ................................................................................................................ 82

Annex 4 ................................................................................................................ 83

Annex 5 ................................................................................................................ 86

Annex 6 ................................................................................................................ 87

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1. Introduction

Speech technologies are used more and more worldwide, which makes the adaptation of

these tools in different languages an important research path. For an adaptation of this kind,

research in dialects and phonetics is very important. TexAFon (Garrido et al., 2012) is a

multi-functional linguistic processing module primary used for Text-To-Speech (TTS) and the

creation of phonetic dictionaries for Central Catalan and Standard Peninsular Spanish. Fodge

(2014) improved the tool, adding with new capabilities in order for it to be used for

automatic speech recognition (ASR) in several dialects of Spanish.

1.1. ASR

ASR first appeared in the 1930s when Homer Dudley of Bell Laboratories presented a model

for the synthesis and analysis of speech. ASR is technology that allows a computer to identify

words spoken into a microphone or telephone and convert them to written text. In short, a

processing of an ASR begins with a person speaking through a microphone and consequently

producing a waveform, captured by the software. This waveform not only has information

about the words and sentences pronounced by the speaker, but also extraneous sounds and

pauses. The software then has to attempt to decode the speech signal into the best

estimation of a given sentence. For doing so, it first converts the speech signal into a

sequence of vectors, which are measured throughout the duration of the speech signal.

Then, using a syntactic decoder it generates a valid sequence of representations (Rabiner et

al., 2004).

The structure of an ASR includes different modules that will be explained in detail below.

1. A parameter extractor, which extracts the acoustic parameters from the speech

signal.

2. An acoustic recognizer, which identifies the sounds and words extracted from the

module 1.

3. A contextual analyzer, which improves the output of the recognizer with the help of

linguistic information.

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Figure 1 Speech recognition performance.

The parameter extractor usually extracts the relevant acoustic information for recognition

using Mel Frequency Cepstral Coefficients (Mermelstein, 1976). After obtaining these

parameters, the next module uses acoustic models, which are language-specific, to associate

the input signal with a sound chain (either allophones or phonemes). This part is done by

statistical and probabilistic techniques: Hidden Markov Models (Lamel & Gauvain, 2003) are

the ones used nowadays.

The acoustic models are automatically created by the use of huge amounts of speech

corpus, from the biggest number of speakers possible. The corpus needs to cover as much

variation as possible, so as to get a representative model. The output of the recognizer

usually has errors due to the fact that recognition has been conducted only with acoustic

information.

For the reason above, the recognizer needs extra linguistic information so as to minimize

errors. This extra information can be added by means of a pronunciation dictionary,

grammars or a mix of both (language models). Both tools need manual elaboration and

linguistic knowledge. However, language models, which have probabilistic measures of word

appearance in a given context, are created automatically by the use of statistical techniques.

Grammars and language models are nowadays very used for the post-processing of the

output of the recognition module. The former is a set of sentences that are correct and can

be part of a given discourse. The latter uses the context in order to predict the appearance

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frequency of a given set of words (expressed in n-grams) so as to choose the most

appropriate or frequent word to correct the output in the recognition module.

Pronunciation dictionaries will be explained in detail later in this paper.

Since it first appeared, this technology has evolved from a simple machine that only

understands a small set of sounds to a sophisticated system that can respond to natural

language spoken in a fluent way. In the 1980s there were some major advances in statistical

modeling of speech, which helped this technology to have numerous applications in tasks

that require a human-machine interface such as automatic call processing (Juang et al.,

2005) or medical transcriptions. These numerous applications require ASR systems to be

trained to operate in complex environments, both acoustically and linguistically (Hain,

2005).

For this reason, researchers realized that “the enormous variation in pronunciation among

speakers of the same language or even the same language variety constitutes a serious

challenge to automatic speech recognition” (Kessens et al., 2003). Consequently, “in order

to obtain reasonable performance, statistical pattern recognition approaches have

dominated research in this field of speech recognition over recent decades” (Hain, 2005).

As seen in the diagram above, one of the important modules for the correct operation of an

ASR is the post-processing module, where linguistic knowledge is needed so as to create

both pronunciation dictionaries and grammars. In section 1.2., the creation of pronunciation

dictionaries is analyzed in detail.

1.2. Pronunciation dictionaries

There are two types of pronunciation dictionaries: the ones for text-to-speech (TTS) and the

ones for ASR. The first only has the prototypical or standard occurrence of a word, whereas

the second needs all the possible pronunciations in a language, since its mission is to

correctly identify all possible realizations of a word. This variation can be caused by general

context dependency or the speed of uttered speech and speaking style in general (Hanžl &

Pollák, 2009). These authors add that emotions, geographical differences –dialect– or

different meanings of a word are also part of the variation in a language.

Torres (2006) go on the idea that ASR applications are for a general public that are not

professionals in the field of speech technologies and consequently the speech they conduct

has some important characteristics. This fact creates a continuous speech with speakers’

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independence, spontaneous and sometimes in an adverse environment; thus, with a lot of

variation.

When dealing with variation in the field of speech technology, one of the domains that

needs to be improved is related to the phonetics, and especially differences in dialects. As

Strik & Cucchiarini (1999) argues, there are four main decisions when choosing a method for

modeling pronunciation: the type of pronunciation variation, the source of the variation

information, the information representation and the level of modeling, as will be explained

in subsequent sections.

The idea of introducing all possible pronunciations of a word is not feasible from a practical

point of view since adding pronunciation variants can introduce new errors (Wester, 2003).

Thus, variants introduced in the pronunciation dictionary have to be chosen carefully. A

study carried out by Kessens et al. (2003) showed that when “the average number of

variants per word in the lexicon exceeds roughly 2.5, the system with variants starts

performing worse than the baseline system without multiple variants”. For this reason, it is

necessary to find a balance between introducing new variants and not increasing the errors

because “simply adding several alternate pronunciations to the dictionary increases the

confusability of words to the extent that the gains from having them are often more than

nullified” (Byrne et al., 1998).

There are different criteria in order to get this balance and smooth the phonemic

transcription: decision trees (Riley et al., 1999), frequency of occurrence of the variants

(Kessens & Wester, 1997), maximum likelihood criterion (Holter, 1997), confidence

measures (Sloboda & Waibel, 1996) and the degree of confusability between the variants

(Sloboda & Waibel, 1996).

1.2.1. Pronunciation dictionaries by corpus and rules

When creating pronunciation dictionaries, the information source can be data-driven (or

data-derived) or knowledge-based. The first is obtained directly from data, whereas the

knowledge-based information is derived from linguistic studies in which rules have been

formulated (Wester, 2003). Both sources have pros and cons. On the one hand, knowledge-

based information can sometimes be insufficient to cover every variation, since “not all of

the variation that occurs in spontaneous speech has been described” (Wester, 2003), which

is known as undercoverage. Nevertheless, overcoverage can also occur adding variants that

do not figure in a given corpus.

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On the other hand, in data-driven information, as only a corpus is given, there can be more

cases of undercoverage, since it does not generalize to situations other than the one in

question (Strik & Cucchiarini, 1999). However, having a good corpus can also help to

improve those exceptions not collected by a knowledge-based method. A good option

proposed by Strik & Cucchiarini (1999) is to use a method with two stages: a first stage

consisting of a knowledge-based model, because it can easily be exported to new tasks; and

then a data-driven model to get the remaining pronunciation variation.

In short, there are two types of pronunciation dictionaries: by means of a corpus (Byrne et

al., 1998; Al-Haj et al., 2009; Hain, 2005) or by the creation of rules (Hanžl & Pollák, 2009).

The use of one way or another not only depends on the language being modeled, but also

on the availability of sources: large corpora or literature about variation in a given language.

Moreover, the task we want to carry out is important since if the domain is specific, the

vocabulary can be smaller (Torres, 2006). If the chosen method is using rule-based

automatic phonetic transcription, a lot of linguistic knowledge is needed and it gets better

results when rules are as exhaustive as possible.

1.3. Automatic phonetic transcription

We have seen two different methods of creating a pronunciation dictionary: by rules or by

corpus. What makes rule-based methods suitable when creating a pronunciation dictionary

is the fact that rules can generalize to new words not seen in a corpus. Thus, when little data

is available, rule-based methods get better results.

When creating a pronunciation dictionary by rules, it is common to do so by automatic

phonetic transcription. However, some languages do not have a clear correspondence

between graphemes and phonemes, making it more difficult to create rules. Such is the case

of English, a very opaque language in terms of the grapheme-phoneme relationship. In fact,

“un mismo segmento fonológico se puede representar mediante varias formas grafémicas

distintas” (Cuetos, 1989). Moreover, English has a lot of irregularities, which makes it harder

for a rule-based method to function well and it needs a large lexicon of irregularities and few

rules.

On the other hand, Spanish is very transparent with relatively few irregular words and with

an almost absolute correspondence between graphemes and phonemes (Cuetos, 1989). Ríos

(1993) in his work on automatic phonetic transcription also argues that “el carácter

“fonémico” de la ortografía española hace relativamente fácil esa operación frente a lo que

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sucede en otras lenguas, como el francés” and word stress is also predictable. The Spanish

characteristics that make it ideal for automatic phonetic transcription by rules in general

also apply to Catalan.

1.3.1. Automatic phonetic transcription in Catalan and Spanish

Some examples of automatic phonetic transcription in Spanish are Diccionario Electrónico

Fonético del Español (Ríos, 1993) using three main dictionaries; a G2P tool by rules for seven

Spanish dialects (Moreno & Mariño, 1998); the improvement of an existing letter-to-phone

system by adding more phonemes and modifying the rules that were not working

(Bonaventura et al., 1998); or SAGA (Nogueiras & Mariño, 2009).

This last project is a tool for Spanish automatic phonetic transcription using SAMPA

alphabet. It has variants of peninsular Spanish and also Latin American dialects defined in

SALA project (Moreno et al., 1998). SAGA project works by arguments and each module has

to be called independently, which requires some computational knowledge to use it.

Some research done in Catalan is Segre (Pachès et al., 2000), Tecnoparla (Shulz et al., 2008;

Shulz et al., 2009) or the TransLectures-UPV toolkit (del Agua et al., 2012). There is an

existing tool that gives the standard pronunciation of words in five dialects (ReSolc, 2010).

However, this tool does not allow for the creation of a whole text and only gives the

standard pronunciation.

All the aforementioned tools take variation into account in one way or another. However,

they do not enter into details of dialect variation and how to analyze it. The one that

explains the most about dialects is Segre, but this tool does not allow the possibility of

having more than one possible pronunciation per dialect, having to choose one dialect with

one only possible pronunciation at a time, making the creation of a pronunciation dictionary

hard.

In this section we have analyzed how automatic phonetic transcription is implemented in

Spanish and Catalan and we have seen that little is done for variation within the second.

Hence in order to create a good pronunciation dictionary for Catalan it is necessary to have a

tool that allows for the creation of more than one variation of a word at a time.

The main purpose of this project is to get a pronunciation dictionary for Catalan

northwestern dialects. In order to fulfill this goal a tool named TexAFon is used. This tool for

now is only TTS but its structure, which will be explained below, allows the creation of more

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than one pronunciation per word by rules with the idea to create a pronunciation dictionary

suitable for ASR.

1.3.2. TexAFon

TexAFon (Garrido et al., 2012) is a multi-functional linguistic processing module. It is

language dependent, and it has primarily been used for linguistic processing in TTS

applications. Fodge (2014) in her master thesis improved it with new capabilities that allow

the creation of more than one variation per word. She adapted the tool to several dialects of

Spanish. The aim of this work is to introduce different dialects of northwestern Catalan to

TexAFon with the same idea, not only to improve it, but also to enable it to form part of the

post-processing module of an ASR.

TexAFon was fully developed by linguists from Universitat Pompeu Fabra and Barcelona

Media Centre d’Innovació and it is “a set of Catalan/Spanish text processing tools for

automatic normalization, phonetic transcription, syllabication, prosodic segmentation and

stress prediction from text” (Garrido et al., 2012). This is a rule and context based tool and

was used for the creation of this project because of its adaptability and multi-functionality.

This tool was developed using Python and it has a modular architecture, which makes it very

adaptable. According to Garrido et al. (2012) there are three sections (Figure 2):

Figure 2 TexAFon modules (Garrido et al., 2012)

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These sections are a general processing core, which includes the language-independent

procedures; the language packages (for now in Catalan and Spanish), including modules

and dictionaries specific of the language; and the applications, which call the processing

core depending on their needs.

The language packages are the language-dependent resources that TexAFon uses to process

input according to language-specific rules. Garrido et al. (2012) included Standard Castilian

and Central Catalan and Fodge (2014) augmented with six new Spanish dialects: Castilla la

Nueva, Northern Extremeño and Andalucía, Southern Extremeño and Andalucía, Canario and

Spanish spoken by L1 American English speakers.

As we already said, the language packages are the language-dependent resources that

TexAFon uses to process input according to language-specific rules. There are two language-

dependent modules: an lts module for letter-to-sound translation, stress predictions and

syllabification and a textnorm module for text expansion. There are also three different

dictionaries: one for exceptions of the established rules, one for the most common

abbreviations and one for initials that do not follow the standard spelling rules.

The nature of TexAFon forces not only every language but also every dialect to be an

independent module. Thus, common changes in all dialects being adapted need to be done

separately in every module. However, after the adaptation of TexAFon for ASR purposes a

new program was created in order to get a pronunciation dictionary:

‘crea_dicc_fonetico_con_variantes_de_lista_palabras’ (Fodge, 2014). It is used to test the

behavior of the rules in a hypothetical ASR application, since the input of this program is a

list of words and the output is the phonetic transcription of these words into a given dialect.

In the case when there is more than one variety, the output gives all of them.

The existing program ‘proc_ling’ is a TTS-oriented tool that allows you to obtain the phonetic

transcription of a whole text. In this case, the program uses sentences or paragraphs and

returns the phonetic chain. Despite being part of the first version of TexAFon, it was also

adapted so as to transform a text into different Spanish dialects. Unlike the first program,

that gives all possible pronunciations of a word, this program only gives the most frequent

one. In both cases, the output is written phonetically and two different transcriptions can be

chosen: SAMPA or Cereproc. Finally, this program allows for the creation of neutral vs. not

neutral speech. This feature is not currently used, since there is not a lot of information on

this topic in Catalan.

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As Fodge (2014) explains “because of its nature as a rule and knowledge-based tool, the

programming of TexAFon requires previous linguistic knowledge; however, the use of it does

not. Users provide their input, input their desired application, and select the language

package they want to work with”.

1.4. Goals and methodology

Although Catalan is not as widely spoken as Spanish throughout the world, there are some

significant differences among dialects. These differences may have some implications when

creating speech technologies, since Central Catalan is the most spoken but not the only one;

thus, leaving some speakers apart of these kinds of technologies. For this main reason, it is a

good idea to implement language variation in speech technologies such as TexAFon in order

to be able to develop pronunciation dictionaries including variants for ASR purposes.

As Kodge (2014) notes in her thesis that those working with TexAFon wanted to prepare this

tool to generate variants simultaneously and for it to be used for ASR. For this reason, this

project has the same three main goals as Kodge’s , not for Spanish dialects but for Catalan.

1. To create an automatic phonetic transcription system oriented to TTS of dialectal

varieties;

2. To improve TexAFon with new capabilities by equipping it to generate several possible

transcriptions for single words, so that multiple possible pronunciations can be modeled for

ASR use;

3. To develop and prepare TexAFon for use as an automatic pronunciation training system.

To do so, analysis of Catalan dialects bibliography was conducted. After getting this

information, rules were created and implemented in TexAFon. Unlike SAGA (Nogueiras &

Mariño, 2009), which works using arguments, each dialect in TexAFon is an independent

module and needs to be created independently: the exception dictionary of each dialect is

also adapted to differences. In section 2 variants are defined and phenomena is chosen so as

to create rules; in section 3 rules are implemented and limitations are explained; in section 4

results are evaluated; and in section 5 progress is being analyzed.

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2. Definition of variants and selection of phenomena

Depending on the author Catalan is divided into five (Badia, 1981) or six1 (Veny, 1993)

different dialects with some important differences not only among them but also within. As

Colomina (1999) explains “en els darrers anys, l’accés al català a les escoles i als mitjans

audiovisuals de totes les regions […] ha suposat el sorgiment d’alguns problemes de model

de llengua”. In fact, when Fabra created in 1933 a model of Catalan, he did not mention the

oral part. It wasn’t until 1990 that the Institut d’Estudis Catalans (IEC) published a suggestion

of how to pronounce Catalan properly according to different dialects. This publication had

the intention of displaying a standard for each dialect (Institut d’Estudis Catalans, 1990).

However, especially due to reasons of power and politics, Central Catalan has always been

seen as the best dialect. For this reason, a lot of speech technologies tools only take into

account this variety; such is the case of TexAFon.

Dialects are different ways of speaking a language. In fact, first a group of dialects that share

some similar characteristics appears and they are then grouped into a common language.

This variation in a language can be among users or among uses. As the table below shows,

the one we will take into account in this project is the variation among users.

Linguistic variation among users (depending on who the speaker is)

Idiolect variation (depending on idiosyncratic characteristics of each speaker): idiolects

Dialectal variation (depending on

the origin of each speaker): dialects

Historical variation (depending on the historical origin): historical dialects, chronolects or diachronic varieties. Geographic variation (depending on the geographic origin): geographic

dialects, geolects or diatopic varieties. Social variation (depending on the

social origin): social dialects, sociolects or diastratic variety.

Linguistic variation among uses (depending on what the speaker does)

Stylistic variation: registers, diaphasic variety

Table 1 Source: adapted from two tables of Veny & Massanell (2015).

Before discussing which dialects and the particular variations within these dialects to be

included, it is important to note the obstacles to variant selection. Although the main

1 Not a lot of information about Catalan dialects is available; we focus our work in Veny (1993 Veny&Massanell (2015), a recent compilation of all the information accessible and described with) and didactic purposes.

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differences among dialects is clear, there are many barriers to finding precise and concrete

information about phonetic characteristics, especially regarding context and frequency.

As Fodge (2014) posits in her thesis and to some extent can also be seen in Table 1, “barriers

include clashing sources, lack of numerical support data, deciphering dialectal versus social

and situational variants and assuring variants are modelable”. For these reasons, there are

difficulties in deciding which phenomena can or should be chosen for inclusion in this

project. Although this project focuses on variations of the segmental level and especially

geographic, there are also important changes in the morphological, syntactical and lexical

level (Veny, 1993); however, they will not be considered in this work.

The best solution when deciding which phenomena to include is finding numerical data, the

more recent the better, supporting the prevalence of said phenomena. However, such

information is not available in Catalan and the only information we can find is something like

“que es dóna en parlants de més edat”, “al nord” or “al sud” Veny & Massanell (2015). Work

done in Catalan dialectology is only descriptive, but does not give evidence of the real use of

every characteristic.

In this work two main dialects with their subdialects are being considered: Northwestern

and Valencian. The first one is spoken in Andorra, Franja de Ponent, Lleida province, and

southern Tarragona. It has four subdialects: the one spoken in Ribagorça (Ribagorçà), the

one spoken in both Pallars Jussà and Pallars Sobirà (Pallarès), the one spoken in the former

diocese of Tortosa (Tortosí) and the one spoken in the central area (Central Area)2.

The second one is spoken in the autonomous region of Valencia and a small region called

Carxe, which is in the autonomous region of Murcia. It also has four subdialects. In fact,

Veny & Massanell (2015) only describe three, but differences in a region of the third

subdialect have brought us to posit a new one. These dialects are Northern Valencian,

Central Valencian, Southern Valencian and the one spoken in Alicante (Alacantí). As Veny &

Massanell (2015) argue, “la divisió respecte del català nord-occidental se sol fer tenint en

compte la isoglossa de l’imperfet de subjuntiu: que ell cantés (nord-occidental), que ell

cantara (valencià)”3.

2 Since there is no literature in English talking about these differences, a translation is created by the author of this project. 3 Badia (1981) proposes another division caused by the distinction between o and e in the singular first person: jo canto vs. jo cante.

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Hence, in this paper, eight Catalan subdialects are being considered: Ribagorçà, Pallarès,

Tortosí, Central Area, Northern Valencian, Central Valencian, Southern Valencian and

Alacantí. The eight Catalan dialects were chosen for three main reasons: because of the

literature available on these dialects, the lack of TTS and ASR tools that model them, and the

fact they differ a lot from Central Catalan, the only dialect available on TexAFon. Eventually,

these dialects could aid the creation of a pronunciation dictionary for dialects of Catalan

other than Central Catalan.

Although we are considering geographic variation, stylistic variation may appear. The reason

is that sometimes researchers in dialectology record data speaking in a formal manner,

which makes the participants also speak in a formal register, making some features

disappear. However, since our data is recovered from a compilation of analyses, this

problem is not considerable.

Once the variants and their phenomena are determined, we must analyze whether or not

they can be coded in TexAFon. As Fodge (2014) explains “phenomena that exist in syllable-

final position carry with them additional difficulty in modeling due to the ordering of

sections and rules in the current version of TexAFon. Currently, information for syllables is

located after rules for grapheme-to-phoneme modeling. However, there are possibilities for

deducing other means of modeling such phenomena, such as looking at the context in which

syllables are formed and generating additional rules”.

Lastly, since ASR rules must be more versatile than those of TTS in order to understand

different idiolects within a dialect, variants are required for ensuring that the system can

recognize the speech of speakers from these new dialects. However, a frequency threshold

must be set in order to make sure the rules are not overly generic. Fodge (2014) divided her

phenomena into primary and secondary in terms of the frequency of appearance. Since

Veny (1993) do not give information about frequency, but do have some information about

population using it (e.g. elderly) or parts of the territory (e.g. only north) this frequency has

been reduced when some extra information is provided.

The main aim is to include as much variation as possible in order to avoid situations in which

speech cannot be recognized. We will divide our primary and secondary phenomena with

this extra information explained above, giving priority to the ones with no information and

secondary those which are reduced either because the population or the territory who

speak in this way it is smaller.

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2.1. Selected inter-dialectal phenomena

When selecting phenomena from every dialect we must take into account that there are

some characteristics that are shared by the eight varieties. In fact, these shared features

make them part of a bigger group called Western Catalan. These shared characteristics that

do differ from Central Catalan and other dialects from the other group called Eastern

Catalan are all related to vowels.

We will now explain those differences shared with all dialects. First, closed /e/ from Vulgar

Latin is maintained closed [e] as a difference of other dialects where it is opened [ɛ] ([peɾa]).

Moreover, unlike other dialects, there are only five unstressed vowels: [a], [e], [i], [o] and [u]

(p[ɔ]nt but p[o]ntet or v[ɛ]rd but v[e]rdet). Finally, initial unstressed /e/ is pronounced as [a]

when it is part of a locked syllable. These features are summarized in Table 2.

Western dialects Primary phenomena Closed /e/ from Vulgar Latin is maintained closed pera (‘pɛɾəÆpeɾa) Unstressed /ɛ/ and /ɔ/ are pronounced [e] and [o] pontet (pun’tɛtÆpon’tet) verdet (bəɾ’dɛtÆbeɾ’det) Initial unstressed /e/ is pronounced as [a] when part of a locked syllable escola (əskɔləÆaskola) Table 2 Western dialects phenomena

Since there is a bigger division among these eight dialects: Northwestern and Valencian,

individually they also share some characteristics within them. In the case of Northwestern

(Table 3) the initial unstressed /o/ is converted into a diphthong [aw] ([aw]liva) and both

unstressed /ɔ/ and /o/ followed by a stressed /i/ are pronounced as [u] (c[u]nill)4. As Veny &

Massanell (2015) add in their explanation “es dóna l’alternança [o]/[u] segons els pobles i

segons els mots”.

Northwestern dialects Primary phenomena Initial unstressed /o/ is pronounced as [aw] oliva (u’liβəÆaw’liβa) Table 3 Northwestern dialects phenomena

Valencian (Table 4) has some shared characteristics in consonants: /ts/ is palatalized into [tʃ]

(to[tʃ]); there is a falling silent of the intervocalic /ð/ when it comes from the Latin –ATA–

(a[jʃa] for aixada) and both pronunciations are possible when it comes from –ATORE–

(moca[ðo] or moca[o]). As it happens in Tortosí, Romanic TL, DL and [j]L are geminated into 4 This feature is not consistent and it is not widespread along the territory or the users. For this reason, we will not take it into account when creating rules.

19

[ll] or also [l] (bat[ll]e or bat[l]e). Finally, Valencian does not have the phoneme [ʒ] but rather

[dʒ] or [jʒ] in the case of Northern Valencian; this feature has some implications, as some

changes within Valencian subdialects are the devoicing of sibilants.

Valencian dialects Primary phenomena /ts/ is palatalized into [tʃ] tots (totsÆtotʃ) Falling silent of the intervocalic /ð/ when it comes from the Latin –ATA– aixada (ə’ʃaðəÆaj’ʃa ) Possible falling silent of the intervocalic /ð/ when it comes from the Latin –ATORE– mocador (mukə’ðoÆmoka’o or moka’ðo) Romanic groups TL, DL and [j]L do not palatalize, but geminate or are pronounced as [l] batlle (‘baʎ:əÆ’bal:e or bale), motlle (‘mɔʎ:əÆ mɔl:e or mole) Table 4 Valencian dialects phenomena

Despite having these shared features, the structure of TexAFon implies the creation of a

whole new module for every dialect. Thus although we have them in one code, they must be

copied and repeated from one to another. Apart from these characteristics and despite the

aforementioned barriers to phenomena selection, a concrete set of phenomena has been

designated for inclusion in TexAFon for each variant dialect of Catalan. Each of these

phenomena has been translated into pseudo-code rules as a primary step before the Python

code is written. The pseudo-code will be discussed later in section 2.10 and can be consulted

in Annex 1.

2.2. Ribagorçà

It is spoken in Alta and Baixa Ribagorça and in Llitera. Thus, the major part of this dialect is

not spoken in Catalonia, but Aragon. This dialect does share some characteristics with

Pallarès and Tortosí, but not with the Central Area. We will now describe these features

saying which of them are shared by other dialects. In fact, despite not being in a transition

zone between dialects, Ribagorçà does share the characteristics of devoicing all sibilant

sounds with Central Valencian. We will start with the features common in all parts of this

territory and we will later explain secondary features(Veny, 1993). The features of this

dialect are summarized in Table 5.

2.2.1. Primary phenomena

As we will see in Pallarès, there are more [ɛ] than in the rest of Catalan. However, Pallarès

still has more occurrences of this phoneme. Ribagorçà also shares with Pallarès the

characteristic of pronouncing [j] the Latin groups –I–, –D[j]–, –B[j]–, –G[j]–. In the case of this

dialect, when an e or i precede it, it disappears; which does not happen in Pallarès. This

20

dialect is the only one from the Northwestern dialects that does not have voiced sibilants; a

feature shared with Central Valencian. An individual feature of Ribagorçà is the

palatalization of pl, bl, cl, gl qnd fl (Veny&Massanell, 2015).

1. Latin sufix –ARIU is pronounced with a [ɛ]; Example: carrer (kə’reÆka’rɛ)

2. Latin root –ACT– is pronounced either with [ɛ] or [ɛj]; Example: llet (‘ʎetÆ’ʎɛt or ‘ʎɛjt)

3. From the Latin –I–, –D[j]–, –B[j]–, –G[j]–, are all pronounced [j] and not [dʒ] or [ʒ],

except when it is preceded by an e or i, which then it disappears; Examples: pujar

(pu’ʒaÆpu’ja), major (mə’ʒoÆma’jo), passejar (pəsə’ʒaÆpase’a), pitjor (pi’dʒoÆpi’o)

4. Devoicing of sibilants /z/ > /s/, /dz/ > /ts/ and /dʒ/ > /tʃ/; Example: casa (‘kazəÆ’kasa),

tretze (‘tɾedzəÆ’tɾetse), viatge (bi’adʒəÆbi’atʃe)

5. Palatalization of /pl/, /bl/, /cl/, /gl/, /fl/: Example: ploure (‘plɔwɾəÆ‘pʎɔwɾə), blau

(blawÆbʎaw), flor (flɔ 5Æfʎɔ)

6. Final consonant group –rn can be pronounced rn, without n or with a final t; For

example: hivern (iβɛɾnÆ iβɛɾn or iβɛɾ or iβɛɾt)

2.2.2. Secondary phenomena

As Veny & Massanell (2015) explain “a la Ribagorça més occidental, a àtona final llatina s’ha

mantingut com a a tant en posició lliure com travada”. Moreover, in the same territory some

Latin groups explained below are pronounced as [θ]. This characteristic is also part of

Matarranya, a little territory of the Tortosí dialect where some features differ (Veny, 1993).

However, this phenomenon is not consistent, for this reason, we will not take it into account

when creating rules.

1. Unstressed final a in syllable endings do not change into e, they are maintained; For

example: canten (‘kantənÆ’kantan), cares (‘kaɾəsÆ’kaɾas)

Ribagorçà Primary Phenomena Secondary variants Latin suffix –ARIU is pronounced with a [ɛ] carrer (kə’reÆka’rɛ)

Unstressed final a in syllable ending do not change into e, it is maintained canten (‘kantənÆ’kantan), cares (‘kaɾəsÆ’kaɾas)

Latin root –ACT– is pronounced either with [ɛ] or [ɛj] llet (‘ʎetÆ’ʎɛt or ‘ʎɛjt)

From the Latin –I–, –D[j]–, –B[j]–, –G[j]–, all is pronounced [j] and not [dʒ], except when it is preceded by an e or i, which then it disappears pujar (pu’ʒaÆpu’ja), major (mə’ʒoÆma’jo), passejar

5 This is one of many examples where o can both be pronounced as [o] or [ɔ]. See Nadeu&Renwik (in press) for more information about changes in vowels.

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(pəsə’ʒaÆpase’a), pitjor (pi’dʒoÆpi’o) Devoicing of sibilants /z/ > /s/, /dz/ > /ts/ and /dʒ/ > /tʃ/ casa (‘kazəÆ’kasa), tretze (‘tɾedzəÆ’tɾetse), viatge (bi’adʒəÆbi’atʃe)

Palatalization of /pl/, /bl/, /cl/, /gl/, /fl/ ploure (‘plɔwɾəÆ‘pʎɔwɾə), blau (blawÆbʎaw), flor (flɔ

Æfʎɔ)

Final consonant group –rn can be pronounced rn, without n or with a final t

hivern (iβɛɾnÆ iβɛɾn or iβɛɾ or iβɛɾt)

Table 5 Ribagorçà phenomena

2.3. Pallarès

It is spoken in both Pallars Subirà and Pallars Jussà. However, some features of this variety

can also be seen in Andorra and Alt Urgell. Since this territory was difficult to access, this

dialect is very conservative. The main features can be seen in Table 6.


The evolution of this dialect implies that the phoneme [ɛ] is more used than in any other

dialects of Catalan. Although some of these [ɛ] are shared with Ribagorçà, some are unique.

The characteristic of pronouncing [j] the Latin groups of –I–, –D[j]–, –B[j]–, –G[j]–is also

shared with Ribagorçà. It also has a shared feature with dialects from the other group

(Rossellonès and Northern Catalan transition): the disappearance of d in the groups ndr and

ldr.

1. Latin suffix –ARIU is pronounced with a [ɛ]; Example: carrer (kə’reÆka’rɛ)

2. Latin root –ACT– is pronounced with [ɛ]; Example: llet (ʎetÆʎɛt)

3. Latin root –AI– is pronounced with [ɛ]; Example: més (mesÆmɛs)

4. Latin suffix –ORIU is pronounced either with [ɛ] or [e], but not with [o]; Example: voltor

(bul’toÆbol’te or bol’tɛ)

5. From the Latin –I–, –D[j]–, –B[j]–, –G[j]–, all is pronounced [j] and not [dʒ] or [ʒ];

Examples: pujar (pu’ʒaÆpu’ja), major (mə’ʒoÆma’jo), passejar (pəsə’ʒaÆpase’ja),

pitjor (pi’dʒoÆpi’jo)

6. In ndr and ldr groups, d is not pronounced; Example: cendre (‘sɛndɾəÆ’sɛnra), moldre

(‘mɔldɾəÆ’mɔlre)

7. Latin group –NN– is not palatalized; Example: banya (‘baŋəÆ’bana), seny (sɛŋÆsen),

escanyar (əskə’ŋaÆaska’na)

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There are no secondary phenomena within this dialect. Since until the 10th century a

language similar to Basque was spoken, it has a lot of special characteristics that do not

appear in any other dialects, as seen in the primary phenomena (Veny, 1993).

Pallarès Primary phenomena Latin suffix –ARIU is pronounced with a [ɛ] carrer (kə’reÆka’rɛ) Latin root –ACT– is pronounced with [ɛ] llet (ʎetÆʎɛt) Latin root –AI– is pronounced with [ɛ] més (mesÆmɛs) Latin suffix –ORIU is pronounced either with [ɛ] or [e], but not with [o] voltor (bul’toÆbol’te or bol’tɛ) From the Latin –I–, –D[j]–, –B[j]–, –G[j]–, all is pronounced [j] and not [dʒ] or [ʒ] pujar (pu’ʒaÆpu’ja), major (mə’ʒoÆma’jo), passejar (pəsə’ʒaÆpase’ja), pitjor (pi’dʒoÆpi’jo) In ndr and ldr groups, d is not pronounced cendre (‘sɛndɾəÆ’sɛnra), moldre (‘mɔldɾəÆ’mɔlre) Latin group –NN– is not palatalized banya (‘baŋəÆ’bana), seny (sɛŋÆsen), escanyar (əskə’ŋaÆaska’na) Table 6 Pallarès phenomena

2.4. Tortosí

This variety is considered a transition between Northwestern and Valencian dialects and it is

spoken in Baix Ebre and Montsià, but it also includes features that can be seen in Ribera

d’Ebre, Terra Alta and Matarranya. The characteristics of this dialect are summarized in

Table 7.


The phenomena in this dialect are shared with some Valencian dialects, since as explained

above; it is in a transition zone between Nortwestern and Valencian dialects.

1. Approximant articulation of bl and gl; Example: poble (‘pɔbblə 6Æ‘pɔβle), regle

(‘reggləÆ’reɣle)

2. Romanic groups TL, DL and [j]L do not palatalize, but geminate; Example: batlle

(‘ba:ʎəÆ’ba:le), motlle (‘mɔ:ʎəÆ mɔ:le)

6 The one above is the Central Catalan standard pronunciation. However, within this dialect, this word can also be pronounced as [pɔpplə] and [pɔplə]; as is the case with the group gl.

23


These phenomena are located in a small territory called Matarranya, situated between

Aragon and Valencian Country. Since it is isolated, a particular Catalan has been developed

(Veny & Massanell, 2015).

1. Opened preliterate e is pronounced either with [jɛ] or [ja]; Example: mel (mɛlÆmjɛl or

mjal)

Tortosí Primary Phenomena Secondary Variants

Approximant articulation of intervocalic bl and gl

poble (‘pɔbbləÆ‘pɔβle), regle (‘reggləÆ’reɣle) Opened preliterate e is pronounced either with [jɛ] or [ja] mel (mɛlÆmjɛl or mjal)

Romanic groups TL, DL and [j]L do not palatalize, but geminate

batlle (‘ba:ʎəÆ’ba:le), motlle (‘mɔ:ʎəÆ ‘mɔ:le)

Table 7 Tortosí phenomena

2.5. Central Area

It is spoken in Segrià, Pla d’Urgell, les Garrigues and Noguera7. This dialect is the most similar

to the central one, since it is in a transition territory. For this reason, except from the

features shared by all dialects, it only has one primary feature that has to be taken into

account. The features of Central Area dialect are summarized in Table 8.


Since this dialect is the most similar to Central Catalan it only has one primary phenomenon.

The unvoiced final /a/ is pronounced [ɛ]. This difference is in fact semantic, since it serves to

differentiate between a feminine and masculine noun as can be seen in the following

example: mestre [mɛstɾe], mestra [mɛstɾɛ]. Moreover, it also helps to distinguish between

the first and third person in verbs when they share the same form: jo cantava [kantaβɛ], ell

cantava [kantaβe]. The characteristic of pronouncing the singular third person with a final

[e] is common in all Northwestern dialects, not the feature of pronouncing the singular first

person as [ɛ], which is only part of the Central Area.

1. Unstressed final /a/ is pronounced [ɛ]; Example: casa (‘kazəÆ’kazɛ), cantava

(kən’taβəÆkan’taβɛ), mestra (mɛstrəÆmɛstɾɛ)

7 Some authors consider Segarra, Urgell, Ribera and Terra Alta to be part of this subdialect.

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Central Area Primary Phenomena Unstressed final /a/ is pronounced [ɛ] casa (‘kazəÆ’kazɛ), cantava (kən’taβəÆkan’taβɛ), mestra (mɛstrəÆmɛstɾɛ) Table 8 Central Area phenomena

2.6. Northern Valencian

It covers the north of Valencian Country: Ports de Morella, Alt and Baix Maestrat, Alcalatén

and Plana Alta and Baixa. As in the case of Tortosí, this dialect can be seen as a transition

between Valencian and Northwestern dialects. The features of Northern Valencian are

summarized in Table 9.


Although there is a segregation of ix group in all Valencian dialects except Alacantí, Northern

Valencian also despalatalize this group, becoming [js]. Moreover, it has another individual

feature as /dz/ is palatalized into [dʒ].

1. Segregation and despalatalization of ix group into [js]; Example: coix (koʃÆkojs), caixa

(kaʃəÆkajsa)

2. /dz/ sound is palatalized into [dʒ]; Example: tretze (tɾedzəÆ tɾedʒe)


There are some secondary phenomena because some features are only encountered in the

north (/dʒ/>[jʒ]) or in the south (distinction between [v] and [b] and maintenance of final r).

1. Distinction between [v] and [b]; Example: vaca (‘bakəÆ’vaka)

2. Maintenance of final r; Example: fuster (fus’teÆfuster), dir (diÆdir)

3. Intervocalic /dʒ/ is converted into [jʒ]; Example: fetge (‘fedʒəÆfejʒe), pluja

(‘pluʒaÆ’plujʒa)

Northern Valencian Primary Phenomena Secondary Variants Segregation and despalatalization of ix group into [is] coix (koʃÆkois), caixa (kaʃəÆkajsa)

Distinction between [v] and [b] vaca (‘bakəÆ’vaka)

/dz/ sound is palatalized into [dʒ] tretze (tɾedzəÆ tɾedʒe)

Maintenance of final r fuster (fus’teÆfuster), dir (diÆdir)

Intervocalic /dʒ/ is converted into [jʒ] fetge (‘fedʒəÆfejʒe), pluja (‘pluʒaÆ’plujʒa)

Table 9 Northern Valencian phenomena

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2.7. Central Valencian

It is spoken at the central area of Valencian Country. It covers the territory between Palància

and Xúquer rivers. The phenomena of Central Valencian are reviewed in Table 10.


This dialect is the only Valencian one that does not have the difference between [b] and [v],

although in Northern Valencian is only found in the south. As explained above, Central

Valencian does have a shared feature with Ribagorçà, as it does not have any voiced sibilant.

In this case, the final t is maintained, which also happens in Southern Valencian; and final r is

also maintained, which is a shared characteristic of Valencian, although in the Northern

Valencian only happens in the south.

1. Decreasing diphthong [wi] instead of a rising diphthong [uj]; Example: buit (bujtÆbwit)

2. Devoicing of sibilants /z/ > /s/, /dz/ > /ts/ and /dʒ/ > /tʃ/; Example: casa (‘kazəÆ’kasa),

tretze (‘tɾedzəÆ’tɾetse), viatge (bi’adʒəÆbi’atʃe)

3. Maintenance of final t in nt and lt groups; Example: molt (molÆmolt), pont (pɔnÆpɔnt)


5. Segregation of ix group into [jʃ] in final and intervocalic position; Example: coix

(koʃÆkojʃ)


In this dialect there are no secondary phenomena, since all the differences encountered are

part of all the territory and there is no extra information about their use.

Central Valencian Primary Phenomena Decreasing diphthong [wi] instead of a rising diphthong [uj] buit (bujtÆbwit) Devoicing of sibilants /z/ > /s/, /dz/ > /ts/ and /dʒ/ > /tʃ/ casa (‘kazəÆ’kasa), tretze (‘tɾedzəÆ’tɾetse), viatge (bi’adʒəÆbi’atʃe) Maintenance of final t in nt and lt groups molt (molÆmolt), pont (pɔnÆpɔnt) Maintenance of final r fuster (fus’teÆfuster), dir (diÆdir) Segregation of ix group into [jʃ] in final and intervocalic position coix (koʃÆkojʃ) Table 10 Central Valencian phenomena

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2.8. Southern Valencian

It goes from central Valencia to the south. The main characteristic of this dialect, which is

only part of Alacantí, is the vowel harmony. The rest of the features are shared with some

other dialects as we have already seen and are summarized in Table 11.



2. Vowel harmony when there is a stressed [ɛ] or [ɔ] ending with a; Example: terra

(tɛrəÆtɛrɛ), olla (oʎəÆɔʎɔ)


4. Maintenance of final t in nt and lt groups; Example: molt (molÆmolt), pont (pɔnÆpɔnt)


6. Segregation of ix group into [jʃ] in final and intervocalic position; Example: coix

(koʃÆkojʃ)


In this dialect there are no secondary phenomena, since the ones that should be in this

section are a whole new dialect called Alacantí, and are explained below. We considered the

differences to be important; thus, we created a new dialect with some features that are

individual and some others that are shared with other dialects.

Southern Valencian Primary phenomena Decreasing diphthong [wi] instead of a rising diphthong [uj] buit (bujtÆbwit) Vowel harmony when there is a stressed [ɛ] or [ɔ] ending with a terra (tɛrəÆtɛrɛ), olla (oʎəÆɔʎɔ) Distinction between [v] and [b] vaca (‘bakəÆ’vaka) Maintenance of final t in nt and lt groups molt (molÆmolt), pont (pɔnÆpɔnt) Maintenance of final r fuster (fus’teÆfuster), dir (diÆdir) Segregation of ix group into [jʃ] in final and intervocalic position coix (koʃÆkojʃ) Table 11 Southern Valencian phenomena

2.9. Alacantí

Veny & Massanell (2015) include this dialect in Southern Valencian. However, as we said

earlier, it has some characteristics itself that justify the creation of this dialect alone in order

27

to create rules that only occur within this dialect. As the name indicates, it is spoken in

Alicante and its features are summarized in Table 12.

In most cases Alacantí is very similar to Southern Valencian. However, in some cases it has

individual features and in some others, it resembles other Valencian dialects more than the

Southern one. First, it does have vowel harmony or the distinction between [v] and [b].

However, some other features only appear in this dialect: both [ɔw] and [ow] diphthongs are

pronounced [aw] and there is a falling silent of intervocalic d in all contexts. Finally, there is

an important difference with any other Valencian dialect, since the group ix is pronounced

as [ʃ] and not [jʃ]8.



2. Vowel harmony when there is a stressed [ɛ] or [ɔ] ending with a; Example: terra

(‘tɛrəÆ’tɛrɛ), olla (‘oʎəÆ’ɔʎɔ)

3. Both [ɔw] and [ow] diphthongs are pronounced [aw]; Example: pou (powÆpaw), bou

(bɔwÆbaw)

4. Falling silent of intervocalic d; Example: cadira (kə’ðiɾəÆka’iɾa)




Since it is actually a part of another dialect, it only has primary phenomena.

Alacantí Primary phenomena Decreasing diphthong [wi] instead of a rising diphthong [uj] buit (bujtÆbwit) Vowel harmony when there is a stressed [ɛ] or [ɔ] ending with a terra (tɛrəÆtɛrɛ), olla (oʎəÆɔʎɔ) Both [ɔw] and [ow] diphthongs are pronounced [aw] pou (powÆpaw), bou (bɔwÆbaw) Falling silent of intervocalic d cadira (kə’ðiɾəÆka’iɾa) Distinction between [v] and [b] vaca (‘bakəÆ’vaka) Maintenance of final r fuster (fus’teÆfuster), dir (diÆdir)

Table 12 Alacantí phenomena

8 This feature was not added in the list, as changes have to be done in the central Catalan rules. Since this feature is shared with the dialect already present in TexAFon, we decided not to list it.

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2.10. Pseudo-code

After developing all rules in every dialect, the next step was the creation of pseudo-code in

order to start generating the rules implemented afterwards in TexAFon. This pseudo-code is

created using SAMPA symbols and creating more than one output when phenomena are

secondary. A rule of pseudo-code is included in this section and the rest is part of Annex 1.

Unstressed final /a/ is pronounced as [E]: This first example has been written with no

abbreviations for clarity.

# Transforms all final unstressed /a/ into [E]

if character == “a” and next character == “NIL”:

transcribe “E”

3. Implementation

3.1. Overview

After finishing the pseudo-code, it needed to be included in TexAFon as a Python code.

Concretely, the main structure was created in a function called transducer for Central

Catalan and adaptations needed to be done in order to improve the tool for all eight dialects

described above. Not only were some rules changed, but also some others were included as

new rules in some dialects. Moreover, since secondary variants were defined, they were also

a whole new part of this module, which only had one possibility in the first dialect created.

The rules described in transducer only affect those phonemes within a word and how they

interact with one another. When one rule has more than one variant, the first will be the

one used in TTS and the others for ASR use only, although ASR will also use the first variant.

The fonetica_sintactica9 function is for rules that affect how phonemes between words

relate to and affect each other. Although the structure helps to distinguish those

phenomena, it also complicates the creation of rules, as some rules must be split between

the two sections in order to be complete. However, there are the so-called archiphonemes,

a kind of intermediary symbol that helps both functions to be unified.

The transducer is the function that takes orthographic input and transcribes it phonetically.

After that, fonetica_sintactica processes this output (already phonetically transcribed). Thus,

the tool needs both modules to work: one after the other. As explained in section 1.3.2.

9 In the rules of this project there are not such phenomena.

29

there are two different programs: ‘crea_dicc_fonetico_con_variantes_de_lista_palabras’

and ‘proc_ling’. The first one is used to create pronunciation dictionaries and allows more

than one possible pronunciation per word; whereas the other work in paragraphs and gives

the most frequent option of every dialect. In the next section, limitations when creating

rules are recorded.

3.2. Limitations on every dialect

3.2.1. Limitations shared in all dialects

The rule “Closed /e/ from Vulgar Latin is maintained closed” is not regular enough to make

it; thus, exceptions are created in the exceptions dictionary. Taking advantage of the

methodology used when deciding if a stressed e was open or closed, stressed o was also

analyzed and changed when necessary. The pronunciation of these two stressed phonemes

was checked in ReSolc, an online tool developed by Universitat Pompeu Fabra. This tool is

“una plataforma de consulta en línia per a professionals de la llengua oral […] sobre la

pronúncia correcta i adequada del lèxic en sentit ampli” (ReSolc, 2010). It gives information

about the pronunciation of Catalan terms in different dialects: Balear, Central,

Northwestern, Rossellonès and Valencian. Although it does not have a specific division

within Northwestern and Valencian dialects it does give information in general about these

two dialects. Although this methodology has some limitations that need to be taken into

account when analyzing the output, due to the variety of contexts where these sounds can

appear open or closed, for now it is the best option. However, we need to take into account

that when a word is not included in the exception dictionary the output may be erroneous.

There are some allophones that are not recorded by this model of rules. It is the case, for

example of [ɱ] (àmfora), [β] (acaba) or [ɣ] (rega). However, these allophones are

dissimulated due to its context. A good idea would be to improve this project by means of

these allophones in order for it to sound more natural. For now, the noise caused by the use

of these allophones is higher than the benefits of introducing them.

Another limitation that appears in every dialect is the fact that currently TexAFon exceptions

dictionary can only have one variant per word. Thus, those rules that only affect part of one

30

dialect cannot appear as part of the exception dictionary. This is another area where some

improvements could be make10.

3.2.2. Limitations in Pallarès

In the rule that records “Latin group –NN– is not palatalized” it was decided to convert all [ŋ]

into [n], as the most common words with this phoneme come from the Latin group –NN–. As

Rasico (1982) explains “hi ha forms dialectes antigues amb /n/: an, afanar, bana, cana,

cànem, escanar, enganar, sen”.

Another limitation present in this dialect is the rule “From the Latin –I–, –D[j]–, –B[j]–, –G[j]–

all is pronounced [j] and not [dʒ] or [ʒ]”. Not only the occurrences form the Latin forms

above, but all occurrences concerning these two phonemes where changed into [j]. For this

reason, more cases of this phenomenon appear than should. In the case of Ribagorçà,

however, it is not the case, since there are less cases and another rule alters words with

such characteristics: “Devoicing of sibilants /z/ > /s/, /dz/ > /ts/ and /dʒ/ > /tʃ/”.

Pallarès has another limitation when describing the use of opened stressed [ɛ]. The logical

evolution of this phoneme from Latin is a word containing a followed by a semivowel that

evolved into an open e followed by the same semivowel. These two sounds, in most dialects

were later converted into [e]. In Pallarès, however, this evolution stopped in an intermediate

step, conserving an open [ɛ] without the semivowel. In some cases, Ribagorçà did also

preserve this semivowel (e.g. llet). This evolution makes the development of rules for these

phenomena almost impossible; thus, exceptions have to be included in the exception

dictionary.

These exceptions are not easy to obtain, since some Latin suffixes and roots are responsible

for this evolution. The best way to get them, apart from analyzing a big corpus, is first

getting the words from a reverse dictionary, after that, looking up the list of words in Atles

Lingüístic del Domini Català11 maps in the territories covered by Pallarès dialect in order to

see which words do have an [ɛ]. When getting the list of these words, they have to be

included. However, there is not a single root or suffix that evolved as such. For this reason, a

10 Due to limitations in time, in the case that a rule had more than one possible realization, those varieties that were similar to the original exception dictionary were preserved. 11 Atles Lingüístic del Domini Català is a dialectology project developed by Institut d’Estudis Catalans. The aim is to put in this atlas linguistic information recorded from 1964 to 1978 in order for it to be available for researchers. The atlas includes the whole Catalan geographical area (Badia, Veny & Pons, 1993; Veny&Pons, 2001).

31

lot of research needs to be done and due to the lack of time, these exceptions will not be

included in this project, but can be part of an improvement of TexAFon in the future.

This last limitation includes up to four different rules: “Latin suffix –ARIU is pronounced with

a [ɛ]”, “Latin root –ACT– is pronounced with [ɛ]”, “Latin root –AI– is pronounced with [ɛ]”,

“Latin suffix –ORIU is pronounced either with [ɛ] or [e], but not with [o]”. The first two rules

are also part of Ribagorçà. In the latter dialect, as explained above, the evolution can give

[ɛj] instead of only [ɛ].

3.2.3. Limitations in Ribagorçà

As explained above, although both Pallarès and Ribagorçà have a feature in common, we

treated them differently because another rule alter words with [ʒ] only in Ribagorçà and not

in Pallarès. Thus, words that fit in the rule “From the Latin –I–, –D[j]–, –B[j]–, –G[j]– all is

pronounced [j] and not [dʒ] or [ʒ]” are put in the exception dictionary and then a rule in

order to devoice the rest of these sibilants is created. This method has the limitation of

perhaps forgetting some words that fit in the first rule and as a result, having some words

mispronounced.

Another limitation in this dialect is in the rule “unstressed final a in syllable endings do not

change into e, they is maintained”. Since it is a morphological rule and only applies in plural

([kaɾas]) or some verb endings ([kantan]) for the current architecture of TexAFon it cannot

be adapted. A morphological tagger and a part-of-speech tagger would play a crucial role

when recognizing and creating rules for this phenomena.

3.2.4. Limitations in Tortosí

Tortosí has the feature of pronouncing /g/ and /b/ as approximants when followed by an /l/.

Since TexAFon does not include these approximants, both rules are cancelled. The intention

was to include these phonemes, but for now, noise will be worse than the benefits. Another

limitation is the rule “open preliterary e is pronounced either with [jɛ] or [ja]”, since it

cannot be expressed as a rule, but put in the exception dictionary. However, this

phenomenon is a secondary phenomenon and for now only one variant per word can be

added in the dictionary; thus, this rule cannot be included neither as a rule, nor in the

dictionary. Another improvement of TexAFon would be to allow the exception dictionary to

have more than one variant per word.

32

3.2.5. Limitations in Central Area

There are two main rules regarding the transformation of a final /a/ sound into [ɛ]. One is

related to all types of nouns, which is a rule that only happens in this dialect and it is easy to

model. However, there is a second apparition of this rule that has to do with morphosyntax,

as for the distinction between the first ([ɛ]) and third ([e]) singular person. In this case, since

Catalan is a language without subject, it is difficult to model it only by rules. For this reason,

in this project, only the first case will be taken into account, hoping for future projects to

solve this problem.

3.2.6. Limitations in Northern Valencian

As seen in Pallarès (section 3.2.2.), the evolution of Latin suffixes and roots give different

pronunciations in different Catalan dialects. In the case of –ATORE suffix, it is not clear

whether the Latin evolution or the substrate language spoken in this zone before Catalan

caused it. Irrespective of the cause, there is not an easy way to create a rule for these words.

Moreover, since in this case both pronunciations are possible, we have decided that the one

preserving the intervocalic d will be the only one taken into account. This characteristic is

common in all Valencian languages excepts for Alacantí where all intervocalic d disappear.

On the other hand, a completely different action is done in the case of –ATA suffix. Since

most of the endings in –ada do silence the intervocalic d, a rule was created to make this

phone disappear. Again, this rule is created for all Valencian dialects excepts for Alacantí.

This dialect has more than one secondary variant. In some words, two different secondary

variants appear, producing up to four variants for the same word (major as [maj’ʒo],

[maj’ʒor], [ma’dʒo] or [ma’dʒor]). despite being an applied work, this project is based on

theoretical information; thus, we are not able to know if anyone produces some of the

variants.

3.2.7. Limitations in Central Valencian

The rule “decreasing diphthong [wi] instead of a rising diphthong [uj]” that also affects

Southern Valencian and Alacantí cannot be recorded by TexAFon module lts. There is a

module in charge of syllable segmentation that creates diphthongs and hiatus. In this

project, this module will not be changed. For this reason, this rule will not be regarded in any

of the three subdialects where it appears.

33

3.2.8. Limitations in Southern Valencian

Although vowel harmony in [ɛ] and [ɔ] does follow a clear patter, we have already seen that

there is a problem when deciding if /e/ and /o/ are open or closed by rules, since it is not a

regular phenomenon and it can change from one person to another (Nadeu & Renwik, in

press). As this feature is only present when an open [ɛ] or [ɔ] are followed by an unstressed

final a, and not if the sound is either [e] or [o] (Jiménez, 2001), if a word is pronounced with

a closed /e/ in this dialect (e.g. pera) and the rule for e does not pronounce this word

properly, the vowel harmony rule will not work correctly. For this reason, a list of words that

do have vowel harmony was recorded from Jiménez (2001) in order to be included in the

exceptions dictionary. Moreover, those exceptions already present in the dictionary were

also changed.

3.2.9. Limitations in Alacantí

As in the case of Southern Valencian, rules regarding vowel harmony have been

incorporated in the exception dictionary. For this reason, some words may be left out and

mispronounced in these two subdialects. Again, as in the case of Southern Valencian, rules

for vowel harmony were not created but put in the exceptions dictionary instead.

3.3. Exception dictionaries

The former version of TexAFon Catalan dialect included an exception dictionary in order to

introduce as much vocabulary as possible that did not follow the expressed rules in lts.

However, the words included have the pronunciation of Central Catalan, which includes a

phone that is not present either in Northwestern dialects nor in Valencian dialects: schwa.

For this reason these terms were adapted to the dialects within this project.

The main terms included in the dictionary are those with a final r that is pronounced,

adverbs ending in –ment and some words that have opened or closed stressed e and o

without any regular rule. The first case does not have any implication in most Valencian

dialects, as final r is pronounced. However, adverbs and stressed e and o do have

implications in all dialects. The first because a stressed syllable is not regularly positioned.

The second because the change from Vulgar Latin /e/ to Central Catalan /ɛ/ is not without

exceptions, but seem to lack a systematic character (Rasico, 1982) .

Another difference between the dialects in this project and Central Catalan is the

pronunciation of most Spanish names. As Northwestern and Valencian dialects do not have

34

schwa, there is not a problem in pronouncing the majority of Spanish names that do not

include phonemes such as [θ] or [x]. The methodology in order to create a pronunciation

dictionary for each dialect in the project was the following:

1. To get all terms in Central Catalan exceptions dictionary.

2. To create rules common in all eight dialects in the project12.

3. To run the list in (1) with the rules in (2).

4. To analyze the output in (3) to see which terms were not necessary and which terms had

to be modified.

5. To remove those terms that did not have any problem.

6. To change the pronunciation of those terms that were not correct13.

As a first attempt, the exception dictionary was common for all dialects in our project (the

one of Central Catalan). However, the main intention is to introduce variation within each

dialect. For this reason, all those features easy to implement using regular expressions were

then changed in each exception dictionary in particular. One example is the pronunciation of

the final unstressed a as [ɛ] in the Central Area or the pronunciation of all final r in some

Valencian dialects.

The rule common in all northwestern dialects: “Initial unstressed /o/ is pronounced as [aw]”

was not changed in any of the dialect exception dictionary. Another rule that was not

changed in the exception dictionary and is part of all eight dialects is “Initial unstressed /e/ is

pronounced as [a] when part of a locked syllable” as it was not present in ReSolc.

Since the main purpose of this project is to be open access, a good idea would be to get

feedback from users and introduce to the exception dictionary those words mispronounced.

In this way, the project would take advantage from users all over the Catalan territory to

improve the tool.

4. Evaluation

In order to evaluate changes in TexAFon two different data sets were analyzed. One

regarding changes intra-word and the other cross-word. The first is the one got from the

12 Although the rule that preserves the final r is only part of Valencian dialects, it was included as the majority of the exceptions ending in r where there because it was not silent and it was easier to remove those that did not need to be pronounced. 13 As seen in section 3.2.1 the methodology to change pronunciations is looking up words in ReSolc to see how words are pronounced both in Northwestern and Valencian dialects.

35

program ‘crea_dicc_fonetico_con_variantes_de_lista_palabras’ and is a list of words with its

phonetic transcription correspondence. The second data is from the other program

‘proc_ling’ and the output analyzed is a whole paragraph. Concretely, two lists of words with

two or more occurrences of every phenomenon were analyzed, making up to 99 different

words (Annex 3); and two different paragraphs created specifically for this project, with up

to 511 words (Annex 5), some of them repeated but in different contexts –with a voiced or

unvoiced phoneme afterwards, etc. Words in lists are chosen in order to test all rules

created, some extra words were also included in order to test the whole tool and not only

new rules. The paragraphs include words in the list so as to see them in different contexts.

To get data for analysis, both the lists and the paragraphs were run. The former in the

program ‘crea_dicc_fonetico_con_variantes_de_lista_palabras’ and the latter in ‘proc_ling’.

These materials were analyzed in every dialect in order to see the errors. The metric used is

the one presented in Van Bael et al. (2007) “the sum of all phone substitutions (Sub),

deletions (Del) and insertions (Ins) divided by the total number of phones in the reference

transcription (N)”. The number is the percentage of incorrectness of each dialect. Four

different percentages are obtained from each dialect: two lists of words and two

paragraphs. However, the final result is the mean of all four measures.

Although some phenomena were deleted from the beginning due to different problems (see

3.2), on the evolution, all the errors are analyzed. In the next section every dialect is

analyzed and the errors are explained so as to be improved in future projects. All the errors

are substitutions and there is not any deletion or insertion.

4.1. Evaluation of Ribagorçà

The list in table 13 is a summary of the results in Ribagorçà dialect. This dialect is the one

with worse results, since the morphological rule that alters the endings in some verbs and

plurals is not included; thus, a lot of words are mispronounced14 (e.g. vivien, gallines, olives,

posaven tenien, etc.).

List 1 List 2 Paragraph 1 Paragraph 2 Total 5/60 = 8.3 3/39 = 7.7 25/273 = 9.16 14/238 = 5.9

42/610 = 6.9 8/99 = 8 39/511 = 7.6

Table 13 Results for Ribagorçà in % of incorrectness

14 This rule is part of a secondary phenomenon and should not be analysed as part of the paragraph. However, we considered it to be very important and have many implications so as not to collect it.

36

Most of the errors are expected, since they were not recorded during the creation of the

rules: the phenomena explained above and opened or closed stressed e (e.g. fetge Æ

*‘fɛdʒe). However, there is a phenomenon that was not expected: stress not being well

located, because it is a verb plus an enclitic (treure’s Æ *tɾew’ɾes).

4.2. Evaluation of Pallarès

In the case of Pallarès, what specially makes the errors appear is the fact that the four rules

regarding open e are not included, thus, making some errors appear (e.g. carrer Æ *ka’re,

llet Æ *’ʎet, etc.). Moreover, since we did not go into detail on other occurrences of these

phenomena (see 3.2.2.), maybe some examples are considered as correct in the evaluation

but they are not. Again in this dialect, there is the problem with open and closed stressed e

and the problem with a stress not put in the right position.

List 1 List 2 Paragraph 1 Paragraph 2 Total 8/60 = 13.3 3/39 = 7.7 6/273 =2.2 6/238 = 2.5

23/610 = 3.8 11/99 = 11 12/511 = 2.3

Table 14 Results for Pallarès in % of incorrectness

4.3. Evaluation of Tortosí

In Tortosí there are fewer errors than in Ribagorçà and Pallarès. However, there is an error

that can only be analyzed in the list and not in the paragraphs, since it is part of a secondary

phenomena, thus, it does not appear in the paragraphs: some words such as mel

pronounced as [mjɛl] or [mjal]. Apart from this error, the same errors that appear in Pallarès

are also part of Tortosí: the stress not well located and problems with open and closed e.

List 1 List 2 Paragraph 1 Paragraph 2 Total 6/60 = 10 3/39 = 7.7 3/273 = 1.1 6/238 = 2.5

18/610 = 2.9 9/99 = 9 9/511 = 1.8

Table 15 Results for Tortosí in % of incorrectness

4.4. Evaluation of Central Area

This dialect is the one with the least errors. The ones that are more significant are the ones

related to changes between open and closed stressed e, common to all dialects. The other

error is the stress of a word not located where it should, which is also common to all

dialects.

37


17/610 = 2.8 8/99 = 8 9/511 = 1.8

Table 16 Results for Central Area in % of incorrectness

4.5. Evaluation of Northern Valencian

The problems this dialect presents are the same that appear in Central Catalan. Moreover,

there is the mispronunciation of mocador, which can be either with or without the

pronunciation of the intervocalic d. This rule was not created (see 3.2.6.) and, thus, this error

is expected but is only part of the errors in the list where more than one variant per word is

recorded. In the paragraph only the first variant is selected; thus, it is not incorrect.


18/610 = 2.9 9/99 = 9 9/511 = 1.8

Table 17 Results for Northern Valencian in % of incorrectness

4.6. Evaluation of Central Valencian

This dialect has also the common errors that appears in all dialects and the one presented in

Northern Valencian: the mispronunciation of open or closed stressed e, the stress not well

placed on a word and the pronunciation of intervocalic d in –ATORE suffixes, again only to

be taken into account as an error in the list and not in the paragraph. Moreover, Central

Valencian presents a new error: the diphthong [uj] does not change into [wi].


20/610 = 3.3 10/99 = 10 10/511 = 2

Table 18 Results for Central Valencian in % of incorrectness

4.7. Evaluation of Southern Valencian

This dialect together with Alacantí are the only ones that have as a primary phenomena the

pronunciation of v as [v] and not [b]. This characteristic poses a problem when the previous

character is an n, since this phoneme should be [ɱ] but it is not part of the inventory of

phonemes of TexAFon (e.g. converteix Æ *[komvertɛjʃ]). Moreover, this dialect as the above

Valencian dialects also pronounced wrong in the list words like mocador, since it pronounces

the intervocalic d and does not give a second option without d.

38


22/610 = 3.6 10/99 = 10 12/511 = 2.3

Table 19 Results for Southern Valencian in % of incorrectness

4.8. Evaluation of Alacantí

This last dialect has the problem of open and closed stressed e and the stress not well

located. As in the case of Southern Valencian, there is the problem of the mispronunciation

of n when the next character is a v. In this dialect, moreover, a problem appeared in the

pronunciation of pou since it is pronounced as [pow] instead of [paw]. This case is because

this word is part of the pronunciation dictionary and is not changed there. We have only

analyzed one occurrence of this problem. However, it can be the case that more problems of

this kind appear.


23/610 = 3.8 9/99 = 9 14/511 = 2.7

Table 20 Results for Alacantí in % of incorrectness

4.9. Overview of the results

The results of all dialects vary a little. As can be seen in the tables, results are worse at a list

level rather than at a paragraph level. This is because most of the words in the list were

picked up because they were part of the phenomena being modeled and also, more than

one variant has to be recorded, which does not happen in the case of paragraphs. The worst

result is the one for Ribagorçà dialect with a result of 6.9% of the words wrongly transcribed.

However, this result is still a 93.1% of words well transcribed. Most of the errors were

expected and exposed as limitations (section 3.2.). Finally, despite only getting one

occurrence of this problem, it may be the case that some words are mispronounced due to

the fact that they are part of the exception list and consequently rules do not apply to them.

39

5. Conclusion

All main goals presented in 1.4. were achieved and TexAFon can now be used to transcribe

in eight new dialects of Catalan. Not only can texts be obtained for all dialects but also

pronunciation dictionaries for each dialect can be collected. There is not still a proper

synthetic Catalan voice in order to read the output, but the first step is fulfilled.

The main improvement by our project in comparison with other existing tools is that it can

give more than one variant per word at a time, making it easy to create a pronunciation

dictionary. The functions of this tool cannot be compared with any existing one, since the

most similar project is Segre and it does not give information on evaluation.

As seen above, for now the architecture of TexAFon only allows to run one dialect at a time.

One possible improvement of this tool would be the creation of a program in order to unify

all possible variants of all dialects to get a complete pronunciation dictionary with

information of every dialect. To do so, we need to take into account the idea of improving

the system by giving some variation but not so much as to cause problems (section 1.2.).

There are several problems that have arisen when creating rules. The first one is that

information is not very recent and most of the studies are compilations of materials whose

information was recorded decades ago. Second, most of the bibliography does not have

information about the speakers (age, studies, context, etc.), which has some implications on

the way people speak. TexAFon ‘proc_ling’ program allows for the running of a module of

neutral or not neutral accent (stylistic variation, for example –Section 1.3.2.–), which could

be improved in future projects. Finally, most of the materials do not give information about

frequency, which makes it difficult to decide for primary and secondary phenomena.

After the creation of this project it is clear that languages are always changing and

information is hard to collect without leaving anything out. Speech technologies need to be

updated so as to be as close as possible to real language. For this reason, linguistic

knowledge will be of the utmost importance in the years to come.

40

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Annex 1

Initial Pseudo-code: Central Area

The following phenomena will be modeled for TTS. They are put in order of appearance in TexAFon.

1. Unstressed final /a/ is pronounced as [E]: This first example has been written with no abbreviations for clarity. # Transforms all final unstressed /a/ into [E] if character == “a” and next character == “NIL”: transcribe “E”

2. Initial unstressed /e/ is pronounced as [a] # Transforms all unstressed initial /e/ into [a] when part of a locked syllable if ch == “e” and pch == “NIL” and nch not in “vowels” and nnch not in “vowels”: transcribe “a”

3. Unstressed /E/ is pronounced as [e]: # Transforms all unstressed /E/ into [e] if character == “e”: transcribe “e”

4. Initial unstressed /o/ is pronounced as [aw] # Transforms all unstressed initial /o/ into [aw] when they are not followed by a vowel: if ch == “o” and pch == “NIL” and nch not in “vowels”: transcribe “aw”

5. Unstressed /O/ is pronounced as [o] # Transforms all unstressed /O/ into [o] if ch == “o”: transcribe “o”

Initial Pseudo-code: Pallarès

1. “ndr” and “ldr” groups, “d” is not pronounced: This first example has been written with no abbreviations for clarity. # Deletes /d/ when preceding letters are /n/ or /l/ and the following letter is /r/ if character == “d” and previous character == “n” or previous character == “l” and next character == “r”: transcribe “_”



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4. /Z/ and /dZ/ are pronounced as [j] # Transforms /Z/ and /dZ/ into [j] if ch == “j” and pch == “vowels” and nch == “vowels”:

transcribe “j”15 5. Latin group –NN– is not palatalized

# Transforms /N/ into /n/ if ch == “n” and nch == “y”: transcribe “n”



Initial Pseudo-code: Tortosí

1. Initial unstressed /e/ is pronounced as [a]: This first example has been written with no abbreviations for clarity. # Transforms all unstressed initial /e/ into [a] when part of a locked syllable if character == “e” and previous character == “NIL” and next next character not “NIL”: transcribe “a”


3. Conversion of /tL into [ll] # Transforms /L/ into [ll] when preceded by a /t/ if ch == “l” and nch == “l” and pch == “t” or ppch == “vowels” and nnch == “vowels”: transcribe “l l”



Initial Pseudo-code: Ribagorçà

15 This was the initial pseudocode. However, in order to get all possibilities, we included up to three different rules: one in j (when surrounded by vowels), one in g (when the previous character is an i) and another in t (when the next character is a j followed by a vowel).

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1. Initial unstressed /e/ is pronounced as [a]: This first example has been written with no abbreviations for clarity. # Transforms all unstressed initial /e/ into [a] when part of a locked syllable if character == “e” and previous character == “NIL” and next next character not “NIL”: transcribe “a”


3. Devoicing of sibilant /dZ/ > [tS] # Transforms /dZ/ sound into [tS]16 if ch == “j” and pch == “vowels” and nch == “vowels”: transcribe “tS”17

4. /Z/ and /dZ/ are pronounced as [j]18 # Transforms /Z/ and /dZ/ into [j] if ch == “j” and pch == “vowels” and nch == “vowels”:

transcribe “j” 5. Palatalization of /pl/, /bl/, /cl/, /gl/, /fl/

# Transforms /l/ followed by /p, b, c, g, f/ into [L] if ch == “l” and pch == “p” or pch == “b” or pch == “c” or pch == “g” or pch == “f”: transcribe “L”



8. Final consonant group “rn” can be pronounced “rn”, “r” or “rt” # Leaves final “rn” as such or transforms it into “r” or “rt” if ch == “r” and nch == “n” and nnch == “NIL”: transcribe “rn”19

transcribe “r” transcribe “rt”

9. Devoicing of sibilant /z/ > [s] # Transforms /z/ into [s] if ch == “s” and pch == “vowels” and nch == “vowels”:

16 In Ribagorçà as in Valencian dialects there is no [Z] sound, but [dZ]. However, this phoneme was once devoiced and was converted into [tS], which is now preserved. 17 This was the initial pseudocode. However, in order to get all possibilities, we changed all occurrences of “jh” (in Cereproc) into “sh” inside the rules. 18 This rule interferes with (3). Thus, it was not treated as a rule itself but put in the exception dictionary of this dialect (see 3.2.) 19 TexAFon rules allow skipping a sound: r is used in the place of rn and so on.

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transcribe “s”20 10. Devoicing of sibilant /dz/ > [ts]

# Transforms /dz/ into [ts] if ch == “t” and nch == “z”: transcribe “t”21

Initial Pseudo-code: Northern Valencian22

The following phenomena will be modeled for TTS:

1. Falling silent of the intervocalic /D/ when it comes from the Latin –ATA–: This first example has been written with no abbreviations for clarity. # Silence all /d/ between /a/ excepts the ones than only have one phoneme before. if character == "d" and whole previous in (u"bà", u"cà", u"dà", u"fà", u"gà", u"hà", u"jà", u"kà", u"là", u"mà", u"nà", u"pà", u"rà", u"sà", u"tà", u"và", u"xà", u"zà") and next character == "a" and next next character in "NIL":

transcribe “d” if character == "d" and previous character == u"à" and next character == "a" and next next character == "NIL": transcribe “_”



4. Intervocalic /dZ/ is converted into [jZ]23 # Transforms intervocalic /dZ/ and /Z/ into [jZ] also when a t precedes it if ch == “j” and pch == “vowels” and nch == “vowels”: if ch == “j” and pch == “t” and ppch == “vowels” and nch == “vowels”: if ch == “g” and pch == “t” and ppch == “vowels” and nch == “vowels”: transcribe “jZ” transcribe “dZ”

5. Conversion of /tL into [ll] 20 This is the initial pseudo-code. However, rules in TexAFon do have more occurrences of this phoneme, for this reason all rules regarding s converted into [z] where changed not only in Ribagorçà but also in Central Valencian. 21 Since the general rule in Catalan is voicing a /t/ when the next sound is voiced, this rule is removed instead of creating a new one. Moreover, a rule in order to unvoice z when preceded by a t is created both in Ribagorçà and Central Valencian. 22 There is a rule that covers all Valencian dialects that is not recorded in the rules, since it affects more than one rule: every [ʒ] phoneme is changed into [dʒ]. However, since in Central Valencian this phoneme is devoiced, we did not include this rule in every dialect in the pseudocode but we adapted it in TexAFon code itself. 23 Three different rules were necessary to model this rule.

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# Transforms /L/ into [ll] when preceded by a /t/ if ch == “l” and nch == “l” and pch == “t” or ppch == “vowels” and nnch == “vowels”: transcribe “l l” transcribe “l”


7. Maintenance of final “r” # Preserves the final r if ch == “r” and nch == “NIL”: transcribe “r”

8. /ts/ is palatalized into [tS] # Transforms /ts/ into [tS] if ch == “t” and nch == “s” and nnch == “NIL”: transcribe “tS”

9. “v” is pronounced as [v] # Makes the distinction between [b] and [v] if ch == “v”: transcribe “v”

10. Segregation and despalatalization of “ix” group into [js] # Transforms /S/ into [js] if ch == “x” and pch == “i”: transcribe “js”

11. /dz/ sound is palatalized into [dZ] # Transforms /dz/ into [dZ] if ch == “z” and pch == “t” and nch == “vowel”: transcribe “Z”

Initial Pseudo-code: Central Valencian




2. Initial unstressed /e/ is pronounced as [a]

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# Transforms all unstressed initial /e/ into [a] when part of a locked syllable if ch == “e” and pch == “NIL” and nch not in “vowels” and nnch not in “vowels”: transcribe “a”


4. Devoicing of sibilant /dZ/ > [tS] # Transforms /dZ/ sound into [tS]24 if ch == “j” and pch == “vowels” and nch == “vowels”: transcribe “tS”25 if ch == “t” and nch == “j”: transcribe “t”

5. Conversion of /tL into [ll] # Transforms /L/ into [ll] when preceded by a /t/ if ch == “l” and nch == “l” and pch == “t” or ppch == “vowels” and nnch == “vowels”: transcribe “l l” transcribe “l”



8. Devoicing of sibilant /z/ > [s] # Transforms /z/ into [s] if ch == “s” and pch == “vowel and nch == “vowel”: transcribe “s”

9. /ts/ is palatalized into [tS] # Transforms /ts/ into [tS] if ch == “t” and nch == “s” and nnch == “NIL”: transcribe “tS”

10. Maintenance of final “t” in “nt” and “lt” # Preserves de final “t” if ch == “t” and pch == “n” or pch == “l” and nch == “NIL”: transcribe “t”

11. Devoicing of sibilant /dz/ > [ts]

24 First we need to take into account that in Valencian dialects there is no [Z] sound, so in any context where other dialects have this phoneme, Valencian has [dZ]. Having this in mind, whenever there is this phoneme in Central Valencian, it changes into [tS]. This means that there is not a single rule to create this sound, but a concatenation of rules in order to devoice the /t/ sound previous to [Z]. 25 This was the initial pseudocode. However, in order to get all possibilities, we changed all occurrences of “jh” (in Cereproc) into “sh”.

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# Transforms /dz/ into [ts] if ch == “t” and nch == “z”: transcribe “t”

12. Segregation of “ix” group into [jS] # Transforms /S/ into [jS] if ch == “x” and pch == “i”: transcribe “jS”

Initial Pseudo-code: Southern Valencian









7. /ts/ is palatalized into [tS] # Transforms /ts/ into [tS]

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if ch == “t” and nch == “s” and nnch == “NIL”: transcribe “tS”

8. Maintenance of final “t” in “nt” and “lt” # Preserves de final “t” if ch == “t” and pch == “n” or pch == “l” and nch == “NIL”: transcribe “t”


10. Segregation of “ix” group into [jS] # Transforms /S/ into [jS] if ch == “x” and pch == “i”: transcribe “jS”

Initial Pseudo-code: Alacantí26


1. Falling silent of intervocalic “d”: This first example has been written with no abbreviations for clarity. # Deletes “d” when it is intervocalic if character == “d” and previous character == “vowel” and next character == “vowel” and previous previous character not in “NIL”: transcribe “_”




5. Both [Ow] and [ow] are pronounced [aw] # Transforms [Ow] and [ow] into [aw] if ch == “o” and nch == “u”: transcribe “a”

6. Unstressed /O/ is pronounced as [o] 26 As Alacantí has falling silent of all intervocalic /d/, rules containing Latin –ATA– and –ATORE– are not needed.

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# Transforms all unstressed /O/ into [o] if ch == “o”: transcribe “o”

7. Maintenance of final “r” # Preserves the final r if ch == “r” and nch == “NIL”:

transcribe “r” 8. /ts/ is palatalized into [tS]

# Transforms /ts/ into [tS] if ch == “t” and nch == “s” and nnch == “NIL”: transcribe “tS”


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Annex 2

transducer & fonetica_sintactica Final Python Code: Northern Valencian

#!/usr/bin/python # -*- coding: UTF-8 -*- ##-*- coding: ISO-8859-1-*- import sys, string, re, codecs sys.path.append('/home/eva/tts/bin') from lexicon import llexicon import definiciones def es_vocal(paraula, posicio_caracter): vocals= [u'a',u'á',u'à',u'â',u'ä',u'ã',u'å',u'æ',u'e',u'é',u'è',u'ë',u'ê',u'i',u'í',u'ï',u'ĩ',u'o',u'ò',u'ó',u'õ',u'ð',u'ô',u'ö',u'ø',u'u',u'ú',u'ü',u'ů',u'ũ',u'E',u'Ë',u'O',u'Ö'] vocals_fortes = [u'a',u'á',u'à',u'â',u'ä',u'ã',u'å',u'æ',u'e',u'é',u'è',u'ë',u'ê',u'o',u'ò',u'ó',u'ö',u'ô',u'õ',u'ð',u'ô',u'ö',u'ø',u'E',u'Ë',u'O',u'Ö'] consonants = ['b', 'c', u'ç', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'm', 'n', u'ñ', 'p', 'q', 'r', 's', 't', 'v', 'w', 'x', 'z'] #print "Entro en es_vocal" #print "Paraula:", paraula #print "Caracter:", paraula[posicio_caracter] #print "Posicio caracter:", posicio_caracter sortida = False llargada_paraula = len(paraula) cadena_paraula = "".join(paraula) if paraula[posicio_caracter] in (vocals_fortes): #print "El caracter es vocal" sortida = True if paraula[posicio_caracter] in (u'í',u'ì',u'ï',u'î',u'ĩ',u'ú',u'ù',u'û',u'ů',u'ũ'): #print "El caracter es vocal" sortida = True if paraula[posicio_caracter] == u'ü': sortida = True if posicio_caracter > 0:

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if paraula[posicio_caracter-1] in (u'q', u'g'): #print "El caracter no es vocal" sortida= False if posicio_caracter < (llargada_paraula-1): if paraula[posicio_caracter+1] in (u'e',u'é',u'è',u'i',u'ì',u'í'): #print "El caracter no es vocal" sortida= False if (paraula[posicio_caracter] in ("u", "i")): sortida = True if posicio_caracter < (llargada_paraula-1): if (posicio_caracter == 0) and (paraula[posicio_caracter+1] in (u'a',u'à',u'á',u'e',u'é',u'è',u'o',u'ò',u'ó')): #print "El caracter no es vocal" sortida = False if posicio_caracter < (llargada_paraula-1) and posicio_caracter > 0: if paraula[posicio_caracter] == u'u' and ((paraula[posicio_caracter-1] in (u'q', u'g')) or (paraula[posicio_caracter-1] in (vocals))) and (paraula[posicio_caracter+1] in (u'a',u'à',u'á',u'e',u'é',u'è',u'o',u'ò',u'ó',u'i',u'í')): #print "El caracter no es vocal" sortida = False if paraula[posicio_caracter] == u'i' and (paraula[posicio_caracter-1] in (vocals)) and (paraula[posicio_caracter+1] in (u'a',u'à',u'á',u'e',u'é',u'è',u'o',u'ò',u'ó')) and (posicio_caracter == 0): #print "El caracter no es vocal" sortida = False if (paraula[posicio_caracter-1] in (vocals_fortes)) and (paraula[posicio_caracter+1] not in (vocals)): #print "El caracter no es vocal: abans hi ha una vocal forta" sortida = False if (paraula[posicio_caracter] == u'u') and (paraula[posicio_caracter-1] in (u'i', u'ï', u'u', u'ü')) and (paraula[posicio_caracter+1] not in (vocals)): #print "El caracter no es vocal: es una combinacio iu, uu" sortida = False if (paraula[posicio_caracter] == u'i') and (paraula[posicio_caracter-1] in (u'i', u'ï')) and (paraula[posicio_caracter+1] not in (vocals)): #print "El caracter no es vocal: es una combinacio ii" sortida = False if posicio_caracter < (llargada_paraula-1) and posicio_caracter > 1:

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if not ((paraula[posicio_caracter-2] in (u'g', u'q')) and (paraula[posicio_caracter-1] in [u'u',u'ü'])): if paraula[posicio_caracter] == u'i' and (((paraula[posicio_caracter+1] == u'x') and (paraula[posicio_caracter-1] in (vocals))) or ((paraula[posicio_caracter+1] == u'g') and (posicio_caracter+1==len(paraula)))): #print "El caracter no es vocal: es una i seguida de x o g" sortida = False if (paraula[posicio_caracter] == u'i') and (paraula[posicio_caracter-1] in [u'u',u'ü']) and (paraula[posicio_caracter+1] not in (vocals)): #print "El caracter no es vocal: es una combinacio ui, üi" sortida = False if paraula[posicio_caracter] == u'i' and (paraula[posicio_caracter-1] in (vocals)) and (paraula[posicio_caracter+1] in (u'a',u'à',u'á',u'e',u'é',u'è',u'o',u'ò',u'ó')): #print "El caracter no es vocal" sortida = False if posicio_caracter > 0: if (paraula[posicio_caracter-1] in (vocals_fortes)) and ((posicio_caracter-1) == 0): #print "El caracter no es vocal: abans hi ha una vocal forta en posicio inicial" sortida = False if (paraula[posicio_caracter-1] in (vocals_fortes)) and (posicio_caracter == (llargada_paraula-1)): #print "El caracter no es vocal: abans hi ha una vocal forta i esta en posicio final" sortida = False if (paraula[posicio_caracter-1] in (u'i', u'ï', u'u', u'ü')) and ((posicio_caracter-1) == 0): #print "El caracter no es vocal: es una combinacio iu, uu, ii, ui" sortida = False if (paraula[posicio_caracter] == u'u') and (paraula[posicio_caracter-1] in (u'i', u'ï', u'u', u'ü')) and (posicio_caracter == (llargada_paraula-1)): #print "El caracter no es vocal: es una combinacio iu, uu" sortida = False if (paraula[posicio_caracter] == u'i') and (paraula[posicio_caracter-1] in (u'i', u'ï')) and (posicio_caracter == (llargada_paraula-1)): #print "El caracter no es vocal: es una combinacio ii"

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sortida = False if posicio_caracter > 1: if not ((paraula[posicio_caracter-2] in (u'g', u'q')) and (paraula[posicio_caracter-1] in [u'u',u'ü'])): if (paraula[posicio_caracter] == u'i') and (paraula[posicio_caracter-1] in (u'u', u'ü')) and (posicio_caracter == (llargada_paraula-1)): #print "El caracter no es vocal: es una combinacio iu, uu" sortida = False # Excepcio: casos de 'i' dels sufixos 'isme', 'ista' i plurals if paraula[posicio_caracter] == u'i': if posicio_caracter == (llargada_paraula-2) and posicio_caracter > 0 and cadena_paraula.endswith("ir") and (paraula[posicio_caracter-1] in (vocals)): sortida = True if posicio_caracter == (llargada_paraula-3) and posicio_caracter > 0 and cadena_paraula.endswith("int") and (paraula[posicio_caracter-1] in (vocals)): sortida = True if posicio_caracter == (llargada_paraula-4) and (cadena_paraula.endswith("isme") or cadena_paraula.endswith("ista")): sortida = True if posicio_caracter == (llargada_paraula-5) and (cadena_paraula.endswith("ismes") or cadena_paraula.endswith("istes")): sortida = True if (paraula[posicio_caracter] == u'y'): sortida = False if paraula[posicio_caracter-1] == u'u' and (posicio_caracter == llargada_paraula-1): #print "El caracter y es vocal: esta en un diptongo uy" sortida = True if paraula[posicio_caracter-1] in(consonants) and paraula[posicio_caracter-1] <> "n" and (posicio_caracter == llargada_paraula-1): #print "El caracter y es vocal: esta al final antes de una consonante" sortida = True

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if posicio_caracter < (llargada_paraula-1): if paraula[posicio_caracter-1] in(consonants) and paraula[posicio_caracter-1] <> "n" and paraula[posicio_caracter+1] in(consonants): #print "El caracter y es vocal: esta entre consonantes" sortida = True return sortida def transducer(ch,nch,nnch,pch,ppch,wholep,wholen,style): # it returns a list of lists salida = [] lista_vocales=set([u'a',u'á',u'à',u'e',u'é',u'è',u'E',u'i',u'í',u'ï',u'o',u'ò',u'ó',u'O',u'u',u'ú',u'ü',u'Ë',u'Ö']) lista_consonantes=set([u'b',u'c'u'ç',u'd',u'f',u'g',u'j',u'k',u'l',u'm',u'n',u'p',u'q',u'r',u's',u't',u'v',u'x',u'y',u'z']) pronoms_febles = ["me", "te", "se", "lo", "la", "los", "les", "ne", "ho", "li","hi", "nos", "vos"] combinacions_pronoms = [u"m¬hi", u"t¬hi", u"li¬ho", u"li¬hi", u"li¬n", u"l¬hi", u"la¬hi", u"los¬hi", u"les¬hi", u"se¬m", u"se¬t", u"-se¬l", u"se¬la", u"s¬ho", u"se¬li", u"s¬hi", u"se¬n", u"se¬ns", u"se¬us", u"se¬ls", u"se¬les", u"n¬hi", u"nos¬el", u"nos¬ho", u"nos¬li", u"nos¬hi", u"nos¬en", u"nos¬els", u"nos¬les", u"vos¬em", u"vos¬el", u"vos¬la", u"vos¬ho", u"vos¬li", u"vos¬hi", u"¬vos¬en", u"vos¬ens", u"vos¬els", u"vos¬les", u"me¬ls", u"te¬ls", u"los¬el", u"los¬la", u"los¬ho", u"los¬hi", u"los¬en", u"nos¬els", u"vos¬els", u"los¬els", u"los¬les", u"les¬hi", u"les¬en"] palabra=wholep+ch+wholen #print "palabra: ", palabra llargada_paraula=len(palabra) #print "llargada paraula: ", llargada_paraula llargada_wholep=len(wholep) #print "llargada wholep: ", llargada_wholep posicio_caracter= llargada_wholep #print "posicio caracter: ", posicio_caracter #print "Empiezo a aplicar las reglas" #print "wholep: ", wholep[-1:] if ch == "a": salida.append(["a0",0,True]) return salida # mb -> m; (b en posicion final precedida por m ) if ch=="b" and pch=="m" and nch=="NIL": salida.append(["",0,True]) return salida if ch=="b" and nch=="NIL": salida.append(["B",0,False])

58

return salida if ch=="b" and nch==u"¬": salida.append(["b",0,False]) return salida #bm ->p submis, subfamilia #if ch=="b" and (nch=="m" or nch=="f"): # return ["p",0,True] # (b en posición final) if ch=="b" and nch=="NIL": salida.append(["p",0,False]) return salida # b -> b if ch == "b": salida.append(["b",0,True]) return salida if ch=="c" and nch=="NIL": salida.append(["G",0,False]) return salida if ch=="c" and nch==u"¬": salida.append(["k",0,False]) return salida # c [EI] -> s ; cicle if ch =="c" and (nch=="E" or nch==u"Ë" or nch=="e" or nch==u"é" or nch==u"è" or nch=="i" or nch==u"Í" or nch==u"í"): salida.append(["s",0,True]) return salida #christosi, technicolor ... if ch=="c" and nch=="h" and (nnch=="r" or nnch=="n" or nnch=="t" or nnch=="g"): salida.append(["k",1,False]) return salida # c h -> ch ; chocolate no al final de paraula if ch =="c" and nch=="h" and not nnch=="NIL": salida.append(["sh",1,True]) return salida if ch=="c" and nch=="c" and not (nnch=="E" or nnch==u"Ë" or nnch=="e" or nnch==u"é" or nnch==u"è" or nnch=="i" or nnch==u"Í" or nnch==u"í"): salida.append(["k",1,False]) return salida if ch=="c" and (nch=="k" or nch=="q"): salida.append(["",0,False]) return salida

59

if ch=="c" and (nch=="m" or nch=="d"): salida.append(["g",0,False]) return salida # c -> k ; casa if ch =="c": salida.append(["k",0,True]) return salida # terminacions ment if ch==u"ç" and nch=="m": salida.append(["z",0,True]) return salida # ç -> s if ch==u"ç" and nch in "NIL": salida.append(["Z",0,True]) return salida if ch==u"ç" and nch in u"¬": salida.append(["s",0,True]) return salida if ch ==u"ç": salida.append(["s",0,True]) return salida #print "Llego a las reglas de la d" # caiguda d en -ada. if ch=="d" and wholep in (u"bà", u"cà", u"dà", u"fà", u"gà", u"hà", u"jà", u"kà", u"là", u"mà", u"nà", u"pà", u"rà", u"sà", u"tà", u"và", u"xà", u"zà") and nch=="a" and nnch in "NIL":

salida.append(["d",0,True]) return salida

if ch=="d" and pch==u"à" and nch=="a" and nnch=="NIL":

salida.append(["",0,False]) return salida

#eudald, dividend if ch=="d" and pch in (u'l', u'n', u'r') and nch== u's' and nnch=="NIL": salida.append(["", 0, False]) return salida if ch=="d" and nch=="NIL": salida.append(["D",0,False]) return salida

60

if ch=="d" and nch==u"¬": salida.append(["d",0,False]) return salida if ch=="d" and ((nch==u's' or nch==u'z') and nnch in "NIL"): salida.append(["t Z", 1, False]) return salida if ch=="d" and nch=="s": salida.append(["t s",0,True]) return salida # d madrid final de paraula if ch=="d" and (nch=="NIL" or nch=="-"): salida.append(["t",0,True]) return salida if ch=="d" and nch=="d": salida.append(["d",1,False]) return salida # d -> d if ch =="d": salida.append(["d",0,True]) return salida # regles E #adreça, neci if ch=="E" or ch==u"Ë": #print "he entrat a les regles de la E. Carcter: ", ch ch_prov = u"" if nch in (u't', u'm', u'j', u'g', u'q', u'b'): ch_prov="ee" elif (nch == u'l' and nnch <> u'l') or (wholen in (u'ssa', u'sses')) or (nch ==u's' and nnch not in (u's', u't')) or (nch ==u'n' and nnch <> u't'): ch_prov="ee" elif nch == u'x' and nnch <> u't' and pch <>u't': ch_prov="ee" elif wholen in (u'u', u'us', u'c', u'cs', u'u¬', u'us¬', u'c¬', u'cs¬'): ch_prov= "ee" elif nch== u'r' and nnch in (u'n', u'r', u't', u'd'): ch_prov="ee" elif nch== u'i': ch_prov= "ee" elif nch== u'c' and nnch== u'a': ch_prov= "ee" else:

61

ch_prov="e" if ch=="E": ch_prov = ch_prov + "1" if ch==u"Ë": ch_prov = ch_prov + "2" salida.append([ch_prov, 0, False]) return salida # e -> e if ch=="e" and pch=="NIL" and nch not in lista_vocales and nnch not in lista_vocales:

salida.append(["a0",0,True]) return salida

if ch=="e":

salida.append(["e0",0,False]) return salida if ch=="f" and nch=="f": salida.append(["f",1,False]) return salida # f -> f if ch =="f": salida.append(["f",0,True]) return salida if ch=="g" and pch=="t" and ppch in lista_vocales and nch in lista_vocales:

salida.append(["DJH",0,True]) salida.append(["j JH",0,True]) return salida

if ch=="g" and pch==u'í' and nch in "NIL": salida.append(["DJH", 0, False]) return salida # g [EI] -> jh ; girona if ch=="g" and pch==u'i' and ppch in (lista_vocales) and nch in "NIL": salida.append(["DJH", 0, False]) return salida if ch=="g" and wholep in (u'mi', u'pui') and nch in "NIL": salida.append(["DJH", 0, False]) return salida if ch=="g" and nch=="NIL": salida.append(["G",0,False]) return salida

62

if ch=="g" and nch==u"¬": salida.append(["g",0,False]) return salida if ch =="g" and (nch=="e" or nch==u"E" or nch==u"é" or nch==u"è" or nch=="i" or nch ==u"í"): salida.append(["d jh",0,True]) return salida # g u [EI] -> g ; guerra if ch =="g" and nch=="u" and (nnch=="e" or nnch==u"è" or nnch=="E" or nnch==u"é" or nnch=="i" or nnch ==u"í"): salida.append(["g",1,True]) return salida # ^ g n -> n ; gnomo if ch =="g" and nch=="n" and pch=="NIL": salida.append(["n",1,False]) return salida if ch=="g" and nch=="g": salida.append(["jh jh",1,False]) return salida if ch=="g" and wholep in ([u'mi', u'pui']): salida.append(["t sh",0,False]) return salida if ch=="g" and pch==u'í' and (nch=="NIL" or nch==u'¬'): salida.append(["t sh",0,False]) return salida if ch=="g" and pch==u'i' and ppch in (lista_vocales)and (nch=="NIL" or (nch==u's'and (nnch=="NIL" or nnch==u'¬')) or nch==u'¬'): salida.append(["t sh",0,False]) return salida if ch=="g" and (nch=="f" or nch=="s" or nch=="NIL"): salida.append(["k",0,False]) return salida # g -> g if ch=="g": salida.append(["g",0,True]) return salida # h -> ; if ch=="h": salida.append(["",0,False]) return salida

63

#print "Llego a las reglas de la i" # regles per la i if ch=="i" and (re.match(r"^semi$",wholep) or re.match(r"^anti$",wholep)): salida.append(["i0",0,True]) return salida if ch=="i" and wholen in (u'sme', u'smes', u'sta', u'stes'): salida.append(["i1",0,True]) return salida if ch=="i" and nch=="x"and ppch in (u'q', u'g')and pch ==u'u': salida.append(["i0", 0, False]) return salida if ch=="i" and nch=="g"and nnch=='NIL' and ppch in (u'q', u'g')and pch==u'u' : salida.append(["i0", 0, False]) return salida if ch=="i" and nch=="g" and wholep == u'pu': salida.append(["", 0, False]) return salida if ch=="i" and nch=="g" and nnch=='NIL' and pch in (u'a', u'à', u'e', u'è', u'é', u'o', u'ò', u'ó', u'E', u'O', u'i', u'ï', u'í', u'ú', u'ü'): salida.append(["", 0, False]) return salida if ch=="i" and pch =="u" and ppch not in (u'q', u'g'): salida.append(["j", 0, False]) return salida if ch=="i" and pch in (u'a', u'à', u'e', u'è', u'é', u'o', u'ò', u'ó', u'E', u'O', u'ú', u'i', u'ï', u'í'): salida.append(["j", 0, False]) return salida if ch=="i" and nch in (u'a', u'à', u'e', u'è', u'é', u'o', u'ò', u'ó', u'E', u'O')and pch=='NIL': salida.append(["j", 0, False]) return salida if ch=="i": salida.append(["i0", 0, False]) return salida # j -> jh

if ch=="j" and pch in lista_vocales and nch in lista_vocales: salida.append(["d jh",0,True]) salida.append(["j jh",0,True]) return salida

64

if ch=="j" and pch=="t" and ppch in lista_vocales and nch in lista_vocales:

salida.append(["d jh",0,True]) salida.append(["j jh",0,True]) return salida

if ch=="j": salida.append(["d jh",0,True]) return salida # ^ k n -> n ; anglicanismes if ch=="k" and ppch=="NIL" and nch=="n": salida.append(["n",1,False]) return salida if ch=="k" and nch=="NIL": salida.append(["G",0,False]) return salida if ch=="k" and nch==u"¬": salida.append(["k",0,False]) return salida # [c] k -> ; anglicanismes #if ch=="k" and pch=="c": # return["",0,False] # k -> k if ch=="k": salida.append(["k",0,False]) return salida # l l $ -> l ; castell??? # l l [C] -> l ; castelldefels # lls, ll$: de vegades català de vegades anglés # l l -> J ; llorar

if ch=="l" and nch=="l" and pch=="t" and ppch in lista_vocales and nnch in lista_vocales:

salida.append(["l l",1,True]) salida.append(["l",1,True]) return salida

if ch=="l" and nch=="l": salida.append(["ll",1,True]) return salida if ch=="l" and nch==u"·" and nnch=="l": salida.append(["l",2,True]) return salida # l -> l if ch=="l":

65

salida.append(["l",0,True]) return salida # mn -> n if ch=="m" and nch==u'n' and pch=="NIL": salida.append(["n",1,True]) return salida # m -> m if ch=="m": salida.append(["m",0,True]) return salida # n y -> ny no quan final de paraula !!inyecto conyugal CATALUNYA if ch=="n" and nch=="y": salida.append(["ny",1,False]) return salida # n n -> n ; NO innovación, innecesario # n -> n # anfora, triunfo enmascarar conmovido convengo if ch=="n" and (nch=="f" or nch=="m" or nch=="v" or nch=="b") : salida.append(["m",0,True]) return salida #excepcions per ng if ch=="n" and nch=="g" and (nnch=="m" or nnch=="n" or nnch==u"ñ" or nnch=="b" or nnch=="d" or nnch=="f" or nnch=="z"): salida.append(["n",0,False]) return salida # ng angola ???, camping ching-chang, washington if ch=="n" and nch=="g" and nnch=="NIL": salida.append(["ng",1,False]) return salida if ch=="n" and nch=="n": salida.append(["n",1,False]) return salida if ch=="n": salida.append(["n",0,True]) return salida # ñ -> ny if ch==u"ñ": salida.append(["ny",0,True]) return salida #print "Llego a las reglas de la O"

66

# Regles O if ch=="O" or ch==u"Ö": ch_prov = u"" if pch==u'h': ch_prov="oo" elif nch==u'p': if pch=='r' and ((nnch==u"NIL") or (nnch==u's' and len(wholen) > 2 and wholen[2] =="NIL")): ch_prov="oo" elif nnch==u's' and len(wholep) > 4 and wholep[2]==u'i' and ((wholep[3]=="NIL") or(wholep[3]==u's' and wholep[4]=="NIL")): ch_prov="oo" elif ((nnch==u'i') or (nnch==u'i' and len(wholep) > 2 and wholep[2]==u's')): ch_prov="oo" else: ch_prov = "o" elif nch == u"t" and nnch == u"a" and wholep == "s": #print "wholep: ", wholep ch_prov="o" elif pch == u"s" and nch == u"b" and nnch == u"r" and len(wholen) > 2 and wholen[2] == "e": #print "wholen: ", wholen ch_prov="o" elif pch==u'l' and ppch==u'p': ch_prov="oo" elif nch==u'b' and nnch==u'r': ch_prov="oo" elif nch==u'l' and nnch==u'l': ch_prov="oo" elif nch==u's': if nnch==u'i': ch_prov="oo" elif nnch==u't' and pch <> u'g': ch_prov = "oo" elif nnch==u'c' and pch <> u'f': ch_prov = "oo" elif nnch==u's' and len(wholen) > 2 and wholen[2] == u'i': ch_prov = "oo" elif nnch==u's' and len(wholen) > 2 and wholen[2] == u'o': ch_prov = "oo" elif nnch==u's' and len(wholen) > 2 and wholen[2] == u'a': ch_prov = "oo" elif pch ==u'r': ch_prov = "oo" elif pch ==u'p': ch_prov = "oo" elif pch ==u'l'and ppch==u'c': ch_prov = "oo"

67

else: ch_prov = "o" elif nch==u'n' and (nnch==u'j' or nnch==u'g'): ch_prov="oo" elif ((nch==u'x') or (nch==u'i' and nnch <> u'x')): ch_prov="oo" elif nch==u'r' and nnch in ([u'd',u'l',u'g',u'b',u'c',u't',u'ç',u'i',u'x',u'f']): ch_prov="oo" elif (nch==u'v' or nch==u'b')and (nnch==u'a' or nnch==u'e'): ch_prov="oo" elif nch==u's' and nnch in ([u'e',u'i',u'o']): ch_prov="oo" elif nch==u't' and (nnch==u'g' or nnch==u'j'): ch_prov="oo" elif nch in ([u'f',u'l',u'g',u'c',u'q',u'ç',u'd',u'u',u't',u'j']): ch_prov="oo" else: ch_prov="o" #print "entro en esta regla" if ch=="O": ch_prov = ch_prov + "1" if ch==u"Ö": ch_prov = ch_prov + "2" salida.append([ch_prov, 0, False]) return salida # o -> o if ch=="o" and (nch==u'u' or nch==u'ú'): salida.append(["oo0", 0, False]) return salida if ch=="o": salida.append(["o0",0,True]) return salida if ch=="p" and pch=="m" and nch=="NIL": salida.append(["",0,False]) return salida if ch=="p" and nch=="NIL": salida.append(["B",0,False]) return salida if ch=="p" and nch==u"¬": salida.append(["p",0,False]) return salida

68

# ^ p s -> psicologo ;?? if ch=="p" and nch=="s" and pch=="NIL": salida.append(["s",1,True]) return salida # p h -> f if ch=="p" and nch=="h": salida.append(["f",1,False]) return salida # campsa, preclampsia if ch=="p" and nch=="s" and pch=="m": salida.append(["",0,False]) return salida # p -> p if ch=="p": salida.append(["p",0,True]) return salida # q u [EI] -> k: if ch=="q" and nch=="u" and (nnch=="e" or nnch==u"é" or nnch=="i" or nnch ==u"í"): salida.append(["k",1,True]) return salida # q -> k if ch=="q": salida.append(["k",0,False]) return salida # r r -> rr if ch=="r" and nch==u'¬': salida.append(["r",0,False]) return salida if ch=="r" and nch=="r": salida.append(["rr",1,True]) return salida #rbol,rganos... -> error?? r principi de mot seguida de consonant. if ch=="r" and pch=="NIL" and re.match(u"[bcdfgjhklmnñpqstvxyz]",nch): salida.append(["r",0,False]) return salida # ^ r -> rr # [LNS] r -> rr # coma-ruga if ch=="r" and (pch=="NIL" or pch=="l" or pch=="n" or pch=="s" or pch=="-"):

salida.append(["rr",0,True]) return salida

69

#r->0 a final de paraula if ch=="r" and ((nch =="NIL") or (nch=="s" and nnch=="NIL")): salida.append(["",0, False]) salida.append(["r",0,False]) return salida # r -> r # r end of word: r or rr; if ch=="r": salida.append(["r",0,True]) return salida # Regles per <s>

# nys -> x/j if ch=="s" and pch=="y" and ppch=="n":

salida.append(["JH",0,True]) return salida

if ch=="s" and nch in "NIL": salida.append(["Z",0,True]) return salida # s s -> s if ch=="s" and nch=="s":

salida.append(["s",1,False]) return salida #print "nch :", nch, " pch$ :", pch if ch=="s" and pch not in (u'¬', u'NIL') and nch == u"¬" and (nnch in (lista_vocales) or(nnch==u"h" and len(wholen) > 2 and wholen[2] in (lista_vocales))):

salida.append(["z",0,True]) return salida if ch=="s" and (nch in (lista_vocales) or (nch==u"h" and nnch in (lista_vocales))) and pch in (lista_vocales):

salida.append(["z",0,True]) return salida if ch=="s" and pch in (lista_vocales) and nch == u'¬' and ((nnch in (lista_vocales)) or (nnch=="h" and len(wholen) > 2 and wholen[2] in (lista_vocales))) :

salida.append(["z",0,True]) return salida # [ ^ d e ] s h -> s ; show if ch=="s" and nch=="h":

salida.append(["sh",1,True]) return salida if ch=="s" and nch in (lista_vocales) and wholep in ("meny", "tran", "enfon"):

70

salida.append(["z",0,True]) return salida # s -> s if ch=="s": salida.append(["s",0,True]) return salida #print "Llego a las reglas de la t" # [ ^ p o s ] t -> ; postguerra but postal, postura, postiza ; postre ; postsandinista?? #if ch=="t" and re.match(r"^pos",wholep) and re.match(u"[bcdfgjklmnñpqsvxyz]",nch): #print "Elimino la t" #return ["",0,True] # t t -> t

if ch=="t" and nch=="g": salida.append(["d jh",1,True])

return salida

if ch=="t" and nch=="j": salida.append(["d jh",1,True]) return salida

if ch=="t" and pch in (u'l', u'n', u'r') and nch== u's' and nnch=="NIL": salida.append(["", 0, False]) return salida if ch=="t" and nch == u"¬" and not (nnch in (lista_vocales) or (nnch==u"h" and len(wholen) > 2 and wholen[2] in (lista_vocales)))and pch==u"n": salida.append(["", 0, False]) return salida if ch=="t" and nch=="NIL": salida.append(["D",0,False]) return salida if ch=="t" and nch==u"¬": salida.append(["t",0,False]) return salida if ch=="t" and (nch==u's' or nch==u'z') and nnch in "NIL": salida.append(["t sh", 1, False]) return salida if ch=="t" and nch=="t": salida.append(["t",1,False]) return salida #noms bascos if ch=="t" and nch=="x":

71

salida.append(["",0,False]) return salida #noms catalans: montserrat, sants, montjuic if ch=="t" and (nch=="s" or nch=="j") and pch=="n": salida.append(["",0,False]) return salida if ch=="t" and nch=="l" and nnch=="l" and pch in lista_vocales:

salida.append(["",0,False]) return salida

#etnologo, rítmico, futbol, atlético if ch=="t" and (nch=="n" or nch=="m" or nch=="b" or nch=="d"): salida.append(["d",0,True]) return salida #casos tz: nacionalitzar if ch=="t" and nch==u'z': salida.append(["d",0,True]) return salida #casos tg: fetge if ch=="t" and nch=="g" and nnch in ("e" or u"è" or u"é" or "i" or u"í"): salida.append(["d",0,True])

return salida # t -> t if ch=="t": salida.append(["t",0,True]) return salida # Regles de la u if ch=="u" and pch in (u'a', u'à', u'Á', u'e', u'è', u'é', u'o', u'ò', u'ó', u'E', u'O', u'u', u'ü', u'ú', u'i',u'ï', u'í', u'Ö', u'Ë'): salida.append(["w", 0, False]) return salida if ch== "u" and nch in (u'a', u'à', u'Á', u'o', u'ò', u'ó', u'O', u'Ö') and pch in (u'q', u'g'): salida.append(["w", 0, False]) return salida if ch== "u" and nch in (u'e', u'è', u'é',u'E', u'Ë', u'i', u'í', u'ï') and pch in (u'q', u'g'): salida.append(["", 0, False]) return salida if ch=="u" and nch in (u'a', u'à', u'Á', u'o', u'ò', u'ó', u'O', u'Ö', u'e', u'è', u'é',u'E', u'Ë') and pch=='NIL': salida.append(["w", 0, False])

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return salida if ch=="u": salida.append(["u0", 0, False]) return salida # noms russos if ch=="v" and (pch=="o" or pch=="e") and nch=="NIL": salida.append(["f",0,False]) return salida if ch=="v": salida.append(["b",0,True]) salida.append(["v",0,True]) return salida # w -> w ; excepcio: water if ch=="w": salida.append(["w",0,False]) return salida # V x -> k s ; vocal examen if ch=="x" and pch in (u'i', u'ï', u'í') and nch in "NIL": salida.append(["Z", 0, False]) return salida if ch=="x" and wholep in (u'e', u'Ë', u'E', u'ine') and nch in (lista_vocales): salida.append(["g z", 0, False]) return salida #if ch=="x" and posicio_caracter > 0: #if es_vocal(palabra, posicio_caracter-1) and nch in (lista_vocales): #return ["k s", 0, False] if ch=="x" and posicio_caracter > 0: if es_vocal(palabra, posicio_caracter-1) and not (pch in (u'i',u'í') and ppch == u'u') and (nch in (lista_vocales) or (nch == u'h' and nnch in (lista_vocales))): #print "caracter siguiente: ", nch #print "caracter anterior: ", pch #print "caracter doble anterior: ", ppch salida.append(["k s", 0, False]) return salida if ch=="x" and nch in (u'p', u't', u'k', u'c', u'q', u'f', u's'): salida.append(["k s", 0, False]) return salida if ch=="x" and nch in (u'b', u'd', u'g', u'v', u'z', u'l', u'm', u'n', u'r'): salida.append(["g z", 0, False]) return salida

73

if ch=="x" and pch==u't': salida.append(["t sh", 0, False]) return salida

if ch=="x" and pch=="i": salida.append(["s",0,False]) return salida

if ch=="x" and (((nch=="NIL" or nch==u'¬') and pch not in (u'i', u'ï', u'í') and pch not in (lista_consonantes)) or ((nch=="NIL" or nch==u'¬') and pch in (u'i', u'ï', u'í') and ppch not in (lista_vocales))): salida.append(["k Z", 0, False]) return salida if ch=="x": salida.append(["sh", 0, False]) return salida # y $ -> j hoy ;excepcions: muy # y [V] -> j if ch=="y" and (nch=="NIL" or re.match(u"[aàáeéiíoóuúù]$",nch) or re.match(u"[aàáéeéiíoouúù]",nch)): salida.append(["j",0,True]) return salida # y -> i if ch=="y": salida.append(["i0",0,True]) return salida # z z -> d s ; mezzosoprano ; excepcions: puzzle if ch=="z" and nch=="z": salida.append(["d s",1,False]) return salida # z -> th

if ch=="z" and pch=="t" and nch in lista_vocales: salida.append(["jh",0,True])

return salida if ch=="z" and nch in "NIL": salida.append(["Z",0,True]) return salida if ch=="z" and nch in u"¬": salida.append(["s",0,True]) return salida if ch=="z": salida.append(["z",0,True])

74

return salida if ch=="-": salida.append(["",0,True]) return salida if ch=="'": salida.append(["",0,False]) return salida ## Transcripcion de los caracteres con acento if ch==u"á": salida.append(["a1",0,True,1]) return salida if ch==u"à": salida.append(["a1",0,True,1]) return salida if ch==u"é": salida.append(["e1",0,True,1]) return salida if ch==u"è": salida.append(["ee1",0,True,1]) return salida if ch==u"í": salida.append(["i1",0,True,1]) return salida if ch==u"ó": salida.append(["o1",0,True,1]) return salida if ch==u"ò": salida.append(["oo1",0,True,1]) return salida if ch==u"ú": salida.append(["u1",0,True,1]) return salida ## Transcripcion de los caracteres con acento secundario if ch==u"Á": salida.append(["a2",0,True,1]) return salida if ch==u"É":

75

salida.append(["e2",0,True,1]) return salida if ch==u"È": salida.append(["ee2",0,True,1]) return salida if ch==u"Í": salida.append(["i2",0,True,1]) return salida if ch==u"Ó": salida.append(["o2",0,True,1]) return salida if ch==u"Ò": salida.append(["oo2",0,True,1]) return salida if ch==u"Ú": salida.append(["u2",0,True,1]) return salida ##Transcripcion caracteres con dieresis. if ch==u"ï": salida.append(["i1",0,True,1]) return salida if ch==u"ü" and pch in (u'q', u'g'): salida.append(["w", 0, False]) return salida if ch==u"ü": salida.append(["u1",0,True,1]) return salida ##Transcripcion de los caracteres no catalanes if ch in (u'ã', u'ä', u'â', u'æ',u'å'): salida.append(["a0",0,True,1]) return salida if ch in (u'ë', u'ê'): salida.append(["e0",0,True,1]) return salida if ch in (u'ö', u'õ',u'ð',u'ô', u'ø'): salida.append(["o0",0,True,1]) return salida if ch in (u'ĩ'):

76

salida.append(["i0",0,True,1]) return salida if ch in (u"û", u'ů'): salida.append(["u0",0,True,1]) return salida ##Transcripcion de caracteres especiales if ch==u"¬": salida.append(["",0, False]) return salida #return ["ERROR",0,False] salida.append(["",0,False]) return salida def fonetica_sintactica(cadena_trans): lista_vocales = ["a1", "e1", "ee1", "i1", "o1", "oo1", "u1", "@1", "a2", "e2", "ee2", "i2", "o2", "oo2", "u2", "@2", "a0", "e0", "ee0", "i0", "o0", "oo0", "u0", "@0", "a", "e", "E", "i", "o", "O", "u", "@"] lista_consonantes = ["b", "d", "f", "g", "x", "jh", "k", "l", "ll", "m", "n", "ng", "ny", "p", "r", "rr", "s", "sh", "t", "z", "th", "y"] lista_consonantes_sordas = ["f", "x", "k", "p", "s", "sh", "t", "th"] lista_consonantes_sonoras = ["b", "d", "g", "jh", "l", "ll", "m", "n", "ng", "ny", "r", "rr", "z", "y"] lista_semivocales = ["w", "j"] lista_salida = [] lista_entrada = cadena_trans.split() transcripcion_simbolo = u"" num_simbolos_transcripcion = len(lista_entrada) for contador_simbolos in range(num_simbolos_transcripcion): transcripcion_simbolo_siguiente = u"" transcripcion_simbolo_anterior = u"" transcripcion_palabra_siguiente = u"" transcripcion_palabra_actual = u"" #print "contador simbolo: ", contador_simbolos transcripcion_simbolo = lista_entrada[contador_simbolos] if contador_simbolos < num_simbolos_transcripcion-2: if lista_entrada[contador_simbolos+1]=="#": transcripcion_simbolo_siguiente = lista_entrada[contador_simbolos+2] #print "Transcripcion_simbolo_siguiente: ", transcripcion_simbolo_siguiente else: transcripcion_simbolo_siguiente = lista_entrada[contador_simbolos+1]

77

if contador_simbolos > 0: transcripcion_simbolo_anterior = lista_entrada[contador_simbolos-1] lista_prov = [] contador_inicio_palabra = contador_simbolos hay_inicio_palabra = False while contador_inicio_palabra < num_simbolos_transcripcion and not (hay_inicio_palabra): if lista_entrada[contador_inicio_palabra] == u'#': hay_inicio_palabra = True contador_inicio_palabra = contador_inicio_palabra+1 #print "Inicio de palabra siguiente: ", contador_inicio_palabra contador_final_palabra = contador_inicio_palabra hay_final_palabra = False while contador_final_palabra < num_simbolos_transcripcion and not (hay_final_palabra): if lista_entrada[contador_final_palabra] == u'#': hay_final_palabra = True else: contador_final_palabra = contador_final_palabra+1 #print "Final de palabra siguiente: ", contador_final_palabra for contador_prov in range (contador_inicio_palabra, contador_final_palabra): lista_prov.append(lista_entrada[contador_prov]) transcripcion_palabra_siguiente = "".join(lista_prov) #print "transcripcion palabra siguiente: ", transcripcion_palabra_siguiente lista_prov = [] contador_final_palabra = contador_simbolos hay_final_palabra = False while contador_final_palabra < num_simbolos_transcripcion and not (hay_final_palabra): if lista_entrada[contador_final_palabra] == u'#': hay_final_palabra = True else: contador_final_palabra = contador_final_palabra+1 #print "Final de palabra actual: ", contador_final_palabra contador_inicio_palabra = contador_simbolos hay_inicio_palabra = False

78

while contador_inicio_palabra > 0 and not (hay_inicio_palabra): if lista_entrada[contador_inicio_palabra] == u'#': hay_inicio_palabra = True else: contador_inicio_palabra = contador_inicio_palabra-1 #print "Inicio de palabra actual: ", contador_inicio_palabra for contador_prov in range (contador_inicio_palabra, contador_final_palabra): lista_prov.append(lista_entrada[contador_prov]) transcripcion_palabra_actual = "".join(lista_prov) #print "transcripcion palabra actual: ", transcripcion_palabra_actual #Aqui es donde deben venir las reglas para cambiar los archifonemas en simbolos de alofonos # mas antic, assebentar-vos-en, pocs bars if transcripcion_simbolo=="Z": #print "Transcripcion simbolo es un archifonema" #print "cadena de entrada: ", lista_entrada #print "contador simbolo: ", contador_simbolos #print "Transcripcion simbolo: ", transcripcion_simbolo if transcripcion_simbolo_siguiente in (lista_vocales): transcripcion_simbolo = "z" #print "Transcripcion simbolo final: ", transcripcion_simbolo elif transcripcion_simbolo_siguiente in (lista_consonantes_sonoras): transcripcion_simbolo = "z" #print "Transcripcion simbolo final: ", transcripcion_simbolo else: transcripcion_simbolo = "s" #print "Transcripcion simbolo final: ", transcripcion_simbolo #neix avui, neix dema, neix cansat if transcripcion_simbolo=="JH": #print "Transcripcion simbolo es un archifonema" #print "cadena de entrada: ", lista_entrada #print "contador simbolo: ", contador_simbolos #print "Transcripcion simbolo: ", transcripcion_simbolo if transcripcion_simbolo_siguiente in (lista_vocales): transcripcion_simbolo = "jh" #print "Transcripcion simbolo final: ", transcripcion_simbolo elif transcripcion_simbolo_siguiente in (lista_consonantes_sonoras): transcripcion_simbolo = "jh" #print "Transcripcion simbolo final: ", transcripcion_simbolo else: transcripcion_simbolo = "sh" #print "Transcripcion simbolo final: ", transcripcion_simbolo

79

#ets amic, néts-avis, ets valent if transcripcion_simbolo=="DZ": #print "Transcripcion simbolo es un archifonema" #print "cadena de entrada: ", lista_entrada #print "contador simbolo: ", contador_simbolos #print "Transcripcion simbolo: ", transcripcion_simbolo if transcripcion_simbolo_siguiente in (lista_vocales): transcripcion_simbolo = "dz" #print "Transcripcion simbolo final: ", transcripcion_simbolo elif transcripcion_simbolo_siguiente in (lista_consonantes_sonoras): transcripcion_simbolo = "dz" #print "Transcripcion simbolo final: ", transcripcion_simbolo else: transcripcion_simbolo = "ts" #print "Transcripcion simbolo final: ", transcripcion_simbolo #crucifix amic, crucifix valent if transcripcion_simbolo=="GZ": #print "Transcripcion simbolo es un archifonema" #print "cadena de entrada: ", lista_entrada #print "contador simbolo: ", contador_simbolos #print "Transcripcion simbolo: ", transcripcion_simbolo if transcripcion_simbolo_siguiente in (lista_vocales): transcripcion_simbolo = "g z" #print "Transcripcion simbolo final: ", transcripcion_simbolo elif transcripcion_simbolo_siguiente in (lista_consonantes_sonoras): transcripcion_simbolo = "g z" #print "Transcripcion simbolo final: ", transcripcion_simbolo else: transcripcion_simbolo = "k s" #print "Transcripcion simbolo final: ", transcripcion_simbolo #mig obert, maig-abril, mig joguener if transcripcion_simbolo=="DJH": #print "Transcripcion simbolo es un archifonema" #print "cadena de entrada: ", lista_entrada #print "contador simbolo: ", contador_simbolos #print "Transcripcion simbolo: ", transcripcion_simbolo if transcripcion_simbolo_siguiente in (lista_vocales): transcripcion_simbolo = "d jh" #print "Transcripcion simbolo final: ", transcripcion_simbolo elif transcripcion_simbolo_siguiente in (lista_consonantes_sonoras): transcripcion_simbolo = "d jh" #print "Transcripcion simbolo final: ", transcripcion_simbolo else: transcripcion_simbolo = "t sh" #print "Transcripcion simbolo final: ", transcripcion_simbolo #cap buit

80

if transcripcion_simbolo=="B": #print "Transcripcion simbolo es un archifonema" #print "cadena de entrada: ", lista_entrada #print "contador simbolo: ", contador_simbolos #print "Transcripcion simbolo: ", transcripcion_simbolo if transcripcion_simbolo_siguiente in (lista_consonantes_sonoras): transcripcion_simbolo = "b" #print "Transcripcion simbolo final: ", transcripcion_simbolo else: transcripcion_simbolo = "p" #print "Transcripcion simbolo final: ", transcripcion_simbolo #nit brillant if transcripcion_simbolo=="D": #print "Transcripcion simbolo es un archifonema" #print "cadena de entrada: ", lista_entrada #print "contador simbolo: ", contador_simbolos #print "Transcripcion simbolo: ", transcripcion_simbolo if transcripcion_palabra_actual == "sa1nD" and (transcripcion_palabra_siguiente == "@0n.dre1w" or transcripcion_palabra_siguiente == "@0n.too1.ni0"): transcripcion_simbolo = "t" elif transcripcion_palabra_actual == "bi1nD" and transcripcion_simbolo_siguiente in (lista_vocales): transcripcion_simbolo = "t" elif transcripcion_palabra_actual == "se1nD" and transcripcion_simbolo_siguiente in (lista_vocales): transcripcion_simbolo = "t" elif transcripcion_simbolo_anterior in (u'n', u'l'): transcripcion_simbolo = u"" elif transcripcion_simbolo_siguiente in (lista_consonantes_sonoras): transcripcion_simbolo = "d" #print "Transcripcion simbolo final: ", transcripcion_simbolo else: transcripcion_simbolo = "t" #print "Transcripcion simbolo final: ", transcripcion_simbolo #amic valent if transcripcion_simbolo=="G": #print "Transcripcion simbolo es un archifonema" #print "cadena de entrada: ", lista_entrada #print "contador simbolo: ", contador_simbolos #print "Transcripcion simbolo: ", transcripcion_simbolo if transcripcion_simbolo_siguiente in (lista_consonantes_sonoras): transcripcion_simbolo = "g" #print "Transcripcion simbolo final: ", transcripcion_simbolo else: transcripcion_simbolo = "k" #print "Transcripcion simbolo final: ", transcripcion_simbolo

81

lista_salida.append(transcripcion_simbolo) cadena_salida = " ".join(lista_salida) #print "Cadena_salida: ", cadena_salida return cadena_salida

82

Annex 3

Word Checker (Dialect Independent)

List 1

abominable actualitzar acabada adult adorn aixada amistats any banya batlle blau bou buit cada cadena

caixa cares carrer casa civada coix cuidar dits dona es escola eliminar extern fada fetge

fuster germà hivern llet major meda mel mocador moldre motlle oliva olla opi ou passada

passejar pera pitjor ploure pont pontet pou puig pujar regle roig terra tretze viatge voltor

List 2

adober adorn agullots ametlla anyada assetjar bada baixar bateig bitlla

blanc brasa brou calçotets campanya dissoldre enrajolador escacs font fonteta

gerro líder moix nuada ocult oleat pagable pany plaer salvatge

seda sonoritzar sou torneig trastorn triatge truja volatilitzar ximplejar

83

Annex 4

Sample from Word Checker Results: Northern Valencian

Words (List 1)

Variant 1 Variant 2 Variant 3 Variant 4

abominable a . b o . m i . n a_&quot . b l e

actualitzar a k . t u . a . l i d . Z a_&quot

a k . t u . a . l i d . Z a_&quot r

acabada a . k a . b a_&quot a

adult a . d u_&quot l D adorn a . d o_&quot r n aixada a j . s a_&quot a amistats a . m i s . t

a_&quot t S

any a_&quot J banya b a_&quot . J a batlle b a_&quot l . l e b a_&quot l e blau b l a_&quot w bou b O_&quot w buit b u_&quot j D cada k a_&quot . d a cadena k a . d E_&quot . n

a

caixa k a_&quot j . s a cares k a_&quot . r e Z carrer k a . rr e_&quot k a . rr e_&quot r casa k a_&quot . z a civada s i . b a_&quot a s i v a_&quot a coix k o_&quot j Z cuidar k u j . d a_&quot k u j . d a_&quot r dits d i_&quot t S dona d O_&quot . n a es e Z escola a s . k O_&quot . l a eliminar e . l i . m i . n

a_&quot e . l i . m i . n a_&quot r

extern e k s . t E_&quot r n

fada f a_&quot . d a fetge f E_&quot d . Z e

84

fuster f u s . t e_&quot f u s . t e_&quot r germà d Z e r . m a_&quot hivern i . b E_&quot r n i v E_&quot r n llet L e_&quot D major m a d . Z o_&quot m a j . Z o_&quot m a d . Z

o_&quot r m a j . Z o_&quot r

meda m e_&quot . d a mel m E_&quot l mocador m o . k a . d

o_&quot m o . k a . d o_&quot r

moldre m O_&quot l . d r e motlle m O_&quot l . l e m O_&quot l e oliva o . l i_&quot . b a o . l i_&quot v a olla O_&quot . L a opi O_&quot . p i ou O_&quot w passada p a . s a_&quot a passejar p a . s e d . Z

a_&quot p a . s e j . Z a_&quot

p a . s e d . Z a_&quot r

p a . s e j . Z a_&quot r

pera p e_&quot . r a pitjor p i d . Z o_&quot p i d . Z o_&quot r ploure p l O_&quot w . r e pont p O_&quot n D pontet p o n . t E_&quot D pou p o_&quot w puig p u_&quot DJH pujar p u d . Z a_&quot p u j . Z a_&quot p u d . Z

a_&quot r p u j . Z a_&quot r

regle rr E_&quot . g l e roig rr O_&quot DJH terra t E_&quot . rr a tretze t r e_&quot d . Z e viatge b i . a_&quot d . Z

e v i . a_&quot d . Z e

voltor b o l . t o_&quot v o l . t o_&quot b o l . t o_&quot r

v o l . t o_&quot r

Words (List 2)

Variant 1 Variant 2 Variant 3 Variant 4

baixar b a j . s a_&quot b a j . s a_&quot r

adober a . d o . b e_&quot a . d o . b e_&quot r

adorn a . d o_&quot r n

85

agullots a . g u . L O_&quot t S ametlla a . m E_&quot l . l a a . m E_&quot l

a

anyada a . J a_&quot a assetjar a . s e d . Z a_&quot a . s e d . Z

a_&quot r

bada b a_&quot . d a bateig b a . t E_&quot DJH bitlla b i_&quot l . l a b i_&quot l a blanc b l a_&quot n G brasa b r a_&quot . z a brou b r O_&quot w calçotets k a l . s o . t E_&quot t

S

campanya k a m . p a_&quot . J a dissoldre d i . s O_&quot l . d r e enrajolador a n . rr a d . Z o . l a . d

o_&quot a n . rr a j . Z o . l a . d o_&quot

a n . rr a d . Z o . l a . d o_&quot r

a n . rr a j . Z o . l a . d o_&quot r

escacs a s . k a_&quot k Z font f O_&quot n D fonteta f o n . t E_&quot . t a gerro d Z E_&quot . rr o líder l i_&quot . d e r moix m o_&quot j Z nuada n u . a_&quot a ocult o . k u_&quot l D oleat o . l e . a_&quot D pagable p a . g a_&quot . b l e pany p a_&quot J plaer p l a . e_&quot p l a . e_&quot r salvatge s a l . b a_&quot d . Z

e s a l v a_&quot d . Z e

seda s e_&quot . d a sonoritzar s o . n o . r i d . Z

a_&quot s o . n o . r i d . Z a_&quot r

sou s O_&quot w torneig t o r . n E_&quot DJH trastorn t r a s . t o_&quot r n triatge t r i . a_&quot d . Z e truja t r u_&quot d . Z a t r u_&quot j . Z

a

volatilitzar b o . l a . t i . l i d . Z a_&quot

v o . l a . t i . l i d . Z a_&quot

b o . l a . t i . l i d . Z a_&quot r

v o . l a . t i . l i d . Z a_&quot r

ximplejar S i m . p l e d . Z a_&quot

S i m . p l e j . Z a_&quot

S i m . p l e d . Z a_&quot r

S i m . p l e j . Z a_&quot r

86

Annex 5

Paragraph Checker (Dialect Independent)

Paragraph 1

Hi havia una vegada uns germans que vivien en una casa d’un puig roig a un poble petit amb diverses cares. Tenien un perer que feia moltes peres, queien a terra, les posaven en una caixa i les bullien en una olla: cada any en tenien tretze. De les peres en feien un pastís amb un motlle. També tenien vaques per fer llet, gallines per tenir ous i un hort que conreaven amb una aixada i en cultivaven civada i olives per moldre. En aquell poble només hi havia un adult i era el professor de l’escola, pintada de blau. Un amic tenia un bou coix i amb la banya torta lligat amb una cadena al mig del carrer. Com que no hi havia adults, no hi havia batlle i, en canvi, moltes amistats. A l’hivern feia molt fred i es posaven un mocador al cap i als dits com adorn, travessaven el pontet i el pou per passejar, pujar i marxar de viatge. El poble es quedava buit. Un dia, en el viatge va ploure i el pitjor és que un voltor abominable no els parava de seguir. Van intentar eliminar-lo amb un regle que havia fet un fuster, però no ho van aconseguir. Finalment el germà major va dir de tornar perquè li feia mal el fetge, es trobava malament i només tenien opi. Aquest conte és mentida i és només per actualitzar i provar TexAFon, una eina que converteix el text a parla i que ha estat adaptada a diversos dialectes del català: el pallarès, el ribagorçà, el de l’àrea central, el tortosí, el valencià septentrional, central, meridional i l’alacantí.

Paragraph 2

Aquest és el segon text per provar el funcionament de TexAFon. El conte comença amb un torneig d'escacs on la gent anava per treure's un sou. Com a adorn sempre hi posaven agullots per baixar des del sostre i alguna bitlla. El terra era completament blanc com uns calçotets de seda acabats d'estrenar, ja que cada any un enrajolador s'encarregava de deixar-ho com nou. El líder del torneig, que sempre bada i té dotze anys, mai estava content amb el resultat i li agradava ximplejar i quan perdia estava moix i es tornava salvatge. Un any també era el bateig d'un nen de tres anys i per dinar hi havia brou, carn a la brasa i de postres ametlles. No hi havia aigua, però la gent bevia d'una fonteta amb un gerro i vi d'una bona anyada. Perquè la gent reciclés posaven papereres de triatge i taules plegables. De fet, molts feien campanya a favor del medi ambient. L'any passat hi havia una truja voltant per allà, tot i que estava nuada, i també un porc ocult. Quan un adober els va intentar tancar amb pany i clau van agafar un trastorn i van començar a assetjar la gent. Al final, sense saber com, es van volatilitzar, això va causar un gran plaer entre el públic, que va dissoldre una poció que tenien i van insonoritzar les parets per no molestar els veïns que no estaven de festa.

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Annex 6

Sample from Paragraph Checker Output: Northern Valencian

Paragraph 1

i # a . b i_&quot . a # u_&quot . n a # b e . g a_&quot a # u_&quot n z # d Z e r . m a_&quot n s # k e # b i . b i_&quot . e n # e n # u_&quot . n a # k a_&quot . z a # d u_&quot n # p u_&quot d Z # rr O_&quot d Z # a # u_&quot n # p O_&quot . b l e # p e . t i_&quot t # a_&quot m # d i . b E_&quot r . s e s # k a_&quot . r e s [signo_punto]

t e . n i_&quot . e n # u_&quot n # p e . r e_&quot # k e # f E_&quot . j a # m o_&quot l . t e s # p e_&quot . r e s [signo_coma] k E_&quot . j e n # a # t E_&quot . rr a [signo_coma] l e s # p o . z a_&quot . b e n # e n # u_&quot . n a # k a_&quot j . s a # i_&quot # l e z # b u . L i_&quot . e n # e n # u_&quot . n a # O_&quot . L a [signo_dos_puntos]

k a_&quot . d a # a_&quot J # e n # t e . n i_&quot . e n # t r e_&quot d . Z e [signo_punto]

d e # l e s # p e_&quot . r e z # e n # f E_&quot . j e n # u_&quot n # p a s . t i_&quot z # a_&quot m # u_&quot n # m O_&quot l . l e [signo_punto]

t a m . b e_&quot # t e . n i_&quot . e n # b a_&quot . k e s # p e r # f e_&quot # L e_&quot t [signo_coma] g a . L i_&quot . n e s # p e r # t e . n i_&quot # O_&quot w z # i_&quot # u_&quot n # O_&quot r t # k e # k o n . rr e . a_&quot . b e n # a_&quot m # u_&quot . n a # a j . s a_&quot a # i_&quot # e n # k u l . t i . b a_&quot . b e n # s i . b a_&quot a # i_&quot # o . l i_&quot . b e s # p e r # m O_&quot l . d r e [signo_punto]

e n # a . k E_&quot L # p O_&quot . b l e # n o . m e_&quot z # i # a . b i_&quot . a # u_&quot n # a . d u_&quot l # i_&quot # e_&quot . r a # e l # p r o . f e . s o_&quot # d e # l e s . k O_&quot . l a [signo_coma] p i n . t a_&quot a # d e # b l a_&quot w [signo_punto]

u_&quot n # a . m i_&quot k # t e . n i_&quot . a # u_&quot n # b O_&quot w # k o_&quot j z # i_&quot # a_&quot m # l a # b a_&quot . J a # t O_&quot r . t a # L i . g a_&quot t # a_&quot m # u_&quot . n a # k a . d E_&quot . n a # a l # m i_&quot d Z # d e l # k a . rr e_&quot [signo_punto]

k O_&quot m # k e # n o_&quot # i # a . b i_&quot . a # a . d u_&quot l s [signo_coma] n o_&quot # i # a . b i_&quot . a # b a_&quot l . l e # i_&quot [signo_coma] e n # k a_&quot m . b i [signo_coma] m o_&quot l . t e z # a . m i s . t a_&quot t S [signo_punto]

a # l i . b E_&quot r n # f E_&quot . j a # m o_&quot l # f r e_&quot t # i_&quot # e s # p o . z a_&quot . b e n # u_&quot n # m o . k a . d o_&quot # a l # k a_&quot p # i_&quot # a l z # d i_&quot t S # k O_&quot m # a . d o_&quot r n [signo_coma] t r a . b e . s a_&quot . b e n # e l # p o n . t E_&quot t # i_&quot # e l # p o_&quot w # p e r # p a . s e d . Z a_&quot [signo_coma] p u d . Z a_&quot # i_&quot # m a r . S a_&quot # d e # b i . a_&quot d . Z e [signo_punto]

88

e l # p O_&quot . b l e # e s # k e . d a_&quot . b a # b u_&quot j t [signo_punto]

u_&quot n # d i_&quot . a [signo_coma] e n # e l # b i . a_&quot d . Z e # b a_&quot # p l O_&quot w . r e # i_&quot # e l # p i d . Z o_&quot # e_&quot s # k e # u_&quot n # b o l . t o_&quot # a . b o . m i . n a_&quot . b l e # n o_&quot # e l s # p a . r a_&quot . b a # d e # s e . g i_&quot [signo_punto]

b a_&quot n # i n . t e n . t a_&quot # e . l i . m i . n a_&quot r l o # a_&quot m # u_&quot n # rr E_&quot . g l e # k e # a . b i_&quot . a # f e_&quot t # u_&quot n # f u s . t e_&quot [signo_coma] p e . r O_&quot # n o_&quot # o # b a_&quot n # a . k o n . s e . g i_&quot [signo_punto]

f i . n a_&quot l . m e_&quot n # e l # d Z e r . m a_&quot # m a d . Z o_&quot # b a_&quot # d i_&quot # d e # t o r . n a_&quot # p e r k E_&quot # l i_&quot # f E_&quot . j a # m a_&quot l # e l # f E_&quot d . Z e [signo_coma] e s # t r o . b a_&quot . b a # m a_&quot . l a . m e_&quot n # i_&quot # n o . m e_&quot s # t e . n i_&quot . e n # O_&quot . p i [signo_punto]

a . k E_&quot t # k o_&quot n . t e # e_&quot z # m e n . t i_&quot . d a # i_&quot # e_&quot z # n o . m e_&quot s # p e r # a k . t u . a . l i d . Z a_&quot # i_&quot # p r o . b a_&quot # t e k . s a . f o_&quot n [signo_coma] u_&quot . n a # E_&quot j . n a # k e # k o m . b e r . t E_&quot j z # e l # t e_&quot k s t # a # p a_&quot r . l a # i_&quot # k e # a_&quot # a s . t a_&quot t # a . d a p . t a_&quot a # a # d i . b E_&quot r . s o z # d i . a . l E_&quot k . t e z # d e l # k a . t a . l a_&quot [signo_dos_puntos]

e l # p a . L a . r E_&quot s [signo_coma] e l # rr i . b a . g o r . s a_&quot [signo_coma] e l # d e # l a_&quot . r e . a # s e n . t r a_&quot l [signo_coma] e l # t o r . t o . z i_&quot [signo_coma] e l # b a . l e n . s i . a_&quot # s e p . t e n . t r i . o . n a_&quot l [signo_coma] s e n . t r a_&quot l [signo_coma] m e . r i . d i . o . n a_&quot l # i_&quot # l a . l a . k a n . t i_&quot [signo_punto]

Paragraph 2

a . k E_&quot t # e_&quot z # e l # s e . g o_&quot n # t e_&quot k s t # p e r # p r o . b a_&quot # e l # f u n . s i . o . n a . m e_&quot n # d e # t e k . s a . f o_&quot n [signo_punto]

e l # k o_&quot n . t e # k o . m E_&quot n . s a # a_&quot m # u_&quot n # t o r . n E_&quot d Z # d e s . k a_&quot k z # o n # l a # d Z e_&quot n # a . n a_&quot . b a # p e r # t r e w . r e_&quot z # u_&quot n # s O_&quot w [signo_punto]

k O_&quot m # a # a . d o_&quot r n # s e m . p r e # i # p o . z a_&quot . b e n # a . g u . L O_&quot t S # p e r # b a j . s a_&quot # d e_&quot z # d e l # s O_&quot s . t r e # i_&quot # a l . g u_&quot . n a # b i_&quot l . l a [signo_punto]

89

e l # t E_&quot . rr a # e_&quot . r a # k o m . p l E_&quot . t a . m e_&quot n # b l a_&quot n k # k O_&quot m # u_&quot n s # k a l . s o . t E_&quot t S # d e # s e_&quot . d a # a . k a . b a_&quot t S # d e s . t r e . n a_&quot [signo_coma] d Z a_&quot # k e # k a_&quot . d a # a_&quot J # u_&quot n # a n . rr a d . Z o . l a . d o_&quot # s e n . k a . rr e . g a_&quot . b a # d e # d e j . s a_&quot r o # k O_&quot m # n O_&quot w [signo_punto]

e l # l i_&quot . d e r # d e l # t o r . n E_&quot t S [signo_coma] k e # s e m . p r e # b a_&quot . d a # i_&quot # t e_&quot # d o_&quot d . Z e # a_&quot J S [signo_coma] m a_&quot j # a s . t a_&quot . b a # k o n . t e_&quot n # a_&quot m # e l # rr e . z u l . t a_&quot t # i_&quot # l i_&quot # a . g r a . d a_&quot . b a # S i m . p l e d . Z a_&quot # i_&quot # k w a_&quot n # p e r . d i_&quot . a # a s . t a_&quot . b a # m o_&quot j z # i_&quot # e s # t o r . n a_&quot . b a # s a l . b a_&quot d . Z e [signo_punto]

u_&quot n # a_&quot J # t a m . b e_&quot # e_&quot . r a # e l # b a . t E_&quot d Z # d u_&quot n # n E_&quot n # d e # t r E_&quot z # a_&quot J Z # i_&quot # p e r # d i . n a_&quot # i # a . b i_&quot . a # b r O_&quot w [signo_coma] k a_&quot r n # a # l a # b r a_&quot . z a # i_&quot # d e # p O_&#37 s . t r E_&quot z # a . m E_&quot l . l e s [signo_punto]

n o_&quot # i # a . b i_&quot . a # a_&quot j . g w a [signo_coma] p e . r O_&quot # l a # d Z e_&quot n # b e . b i_&quot . a # d u_&quot . n a # f o n . t E_&quot . t a # a_&quot m # u_&quot n # d Z E_&quot . rr o # i_&quot # b i_&quot # d u_&quot . n a # b O_&quot . n a # a . J a_&quot a [signo_punto]

p e r k E_&quot # l a # d Z e_&quot n # rr e . s i . k l e_&quot s # p o . z a_&quot . b e n # p a . p e . r e_&quot . r e z # d e # t r i . a_&quot d . Z e # i_&quot # t a_&quot w . l e s # p l e . g a_&quot . b l e s [signo_punto]

d e # f e_&quot t [signo_coma] m o_&quot l t S # f E_&quot . j e n # k a m . p a_&quot . J a # a # f a . b o_&quot r # d e l # m e_&quot . d i # a m . b i . e_&quot n [signo_punto]

l a_&quot J # p a . s a_&quot t # i # a . b i_&quot . a # u_&quot . n a # t r u_&quot d . Z a # b o l . t a_&quot n # p e r # a . L a_&quot [signo_coma] t o_&quot t # i_&quot # k e # a s . t a_&quot . b a # n u . a_&quot a [signo_coma] i_&quot # t a m . b e_&quot # u_&quot n # p O_&quot r k # o . k u_&quot l [signo_punto]

k w a_&quot n # u_&quot n # a . d o . b e_&quot # e l z # b a_&quot # i n . t e n . t a_&quot # t a n . k a_&quot # a_&quot m # p a_&quot J # i_&quot # k l a_&quot w # b a_&quot n # a . g a . f a_&quot # u_&quot n # t r a s . t o_&quot r n # i_&quot # b a_&quot n # k o . m e n . s a_&quot # a # a . s e d . Z a_&quot # l a # d Z e_&quot n [signo_punto]

a l # f i . n a_&quot l [signo_coma] s e_&quot n . s e # s a . b e_&quot # k O_&quot m [signo_coma] e z # b a_&quot n # b o . l a . t i . l i d . Z a_&quot [signo_coma] a j . s O_&quot # b a_&quot # k a w . z a_&quot # u_&quot n # g r a_&quot n # p l a . e_&quot # e_&quot n . t r e # e l # p u_&quot . b l i k [signo_coma] k e # b a_&quot # d i . s O_&quot l . d r e # u_&quot . n a # p o . s i . o_&quot # k e # t e . n i_&quot . e n # i_&quot # b a_&quot n # i n .

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s o . n o . r i d . Z a_&quot # l e s # p a . r E_&quot t S # p e r # n o_&quot # m o . l e s . t a_&quot # e l z # b e . i_&quot n s # k e # n o_&quot # a s . t a_&quot . b e n # d e # f e_&quot s . t a [signo_punto]

Automatic Phonetic Transcription of dialectal variance in Catalan

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