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  • 3P Portuguese Pronunciation Professor

    Mariana Sofia Pimenta Lopes

    Thesis to obtain the Master of Science Degree in

    Electrical and Computer Engineering

    Supervisor: Prof. Isabel Maria Martins Trancoso

    Examination Committee

    Chairperson: Professor Joo Fernando Cardoso Silva Sequeira

    Supervisor: Professor Isabel Maria Martins Trancoso

    Members of the Committee: Professor Hugo Daniel dos Santos Meinedo

    October 2014

    3P

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    To my parents,

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    Acknowledgements

    Acknowledgements

    I am using this opportunity to express my gratitude to everyone who supported me throughout the

    course of this project. I am sincerely grateful for their guidance, constructive criticism and friendly

    advice during the project work.

    I would like to express the deepest appreciation to my advisor Professor Isabel Trancoso for

    encouraging my research and providing priceless support and encouragement when I most needed it.

    I would also like to thank the L2F staff, especially to Professor Hugo Meinedo, Professor

    Alberto Gareta, Professor Thomas Pelligrini and Phd student Anna Pompili for the immense

    assistance, and provision of the source materials essential to helping me completing this project.

    Furthermore, I would like to extend my thankfulness to all the people mentioned in the

    references for making their work available, so people can understand and adapt their research.

    Finally a special thanks to my family. Words cannot express how grateful I am to my mother,

    and father for all of the sacrifices that youve made on my behalf and encouragement to strive towards

    my goal.

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    Abstract

    Abstract

    The quality of oral proficiency forms an important part in learning a foreign language. Yet, frequently

    students find it hard to obtain a reliable source where they can work their pronunciation intensely. An

    automatic assessment system can reduce the cost and workload associated with this task. This type

    of tools are available for students of widely spoken languages such as American or British English,

    however there is not a large amount of them for students of European Portuguese (EP).

    The research presented in this thesis investigates a solution for creating a computer assisted

    language learning (CALL) system for EP using as its base the work of Witt (1999)[1].

    This thesis begins by outlining important aspects for computer-assisted language learning and

    makes a brief analysis of the EP phonemes and the comparison with the two other languages

    presented in the corpus, Spanish and Bulgarian. Then the several steps in the method are explained,

    i.e., firstly the audio speech is digitalized, then, using Audimus, posterior probabilities on 20 ms frames

    are calculated from the extracted features. Subsequently, a GOP score is calculated for each frame

    and for each phoneme. Then the GOP is normalized and using a pre-established threshold, from

    native speakers data, the threshold is adapted in order to improve efficiency in classifying the

    phonemes as a correct or incorrect utterance. Finally, since the threshold is a subjective to who

    implemented it, it is compared with three human judges in order to guarantee its quality.

    Keywords

    CAPT, GOP, normalization, pronunciation, natives, non-natives, European Portuguese

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    Resumo

    Resumo A qualidade de proficincia oral constitui uma parte importante na aprendizagem de uma lngua

    estrangeira. No entanto, muitas vezes os alunos tm dificuldade em obter uma fonte fivel, onde

    podem trabalhar intensamente a sua pronncia. Um sistema de avaliao automtica pode reduzir o

    custo e a carga de trabalho associada a essa tarefa. Este tipo de ferramentas est disponvel para

    estudantes de lnguas mais faladas, como o Ingls americano ou britnico, no entanto no h, em

    grande parte investigao para estudantes de Portugus Europeu (PE).

    A pesquisa apresentada nesta tese investiga uma soluo para a criao de um sistema

    assistido por computador para a aprendizagem de lnguas (CALL) para o PE usando como base o

    trabalho de Witt (1999) [1].

    Esta tese comea por descrever aspetos importantes para a aprendizagem de lnguas

    assistida por computador, fazendo tambm uma breve anlise dos fonemas do EP e uma

    comparao com as outras duas lnguas apresentadas no corpus, o espanhol e o blgaro. Em

    seguida, os vrios passos do processo so explicados, ou seja, em primeiro lugar, o registo udio da

    fala digitalizado, e, utilizando Audimus, as probabilidades posteriores em intervalos de 20 ms so

    calculadas a partir das caractersticas extradas. Subsequentemente, uma pontuao GOP

    calculada para cada intervalo e para cada fonema. Em seguida, o GOP normalizado e usando um

    limite pr-estabelecido, obtido a partir de dados de falantes nativos, o limite adaptado, a fim de

    melhorar a eficincia na classificao dos fonemas como correta ou incorretamente pronunciados.

    Finalmente, uma vez que o limite subjetivo para quem o executou, ele comparado com o

    julgamento de trs juzes humanos, a fim de garantir a sua qualidade.

    Palavras-chave

    CALL, GOP, normalizao, pronncia, nativos, no nativos, Portugus Europeu.

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    Table of Contents

    Acknowledgements ................................................................................... v

    Abstract .....................................................................................................vii

    Resumo.................................................................................................... viii

    List of Figures ........................................................................................... xi

    List of Tables ............................................................................................xii

    List of Acronyms ...................................................................................... xiii

    List of Software ........................................................................................xiv

    1 Introduction ................................................................................... 15

    1.1 Overview ................................................................................................ 16

    1.2 Motivation and problem specification ..................................................... 16

    1.3 Innovations of the work .......................................................................... 16

    1.4 Thesis contents ..................................................................................... 16

    2 Pronunciation ................................................................................ 18

    2.1 Learning word pronunciation ................................................................. 19

    2.2 Automatic Speech Recognition .............................................................. 19

    3 Phonology ..................................................................................... 21

    3.1 European Portuguese ............................................................................ 23

    3.1.1 Brief description of EP ...................................................................................... 23

    3.1.2 Phonology of EP ............................................................................................... 25

    3.2 EP and foreign languages ..................................................................... 28

    3.2.1 Brief comparison with Spanish .......................................................................... 28

    3.2.2 Brief comparison with Bulgarian ........................................................................ 30

    4 System Design.............................................................................. 31

    4.1 State of the art ....................................................................................... 33

    4.1.1 Scientific research ............................................................................................ 33

    4.1.2 Existing tools .................................................................................................... 33

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    4.2 Method................................................................................................... 34

    4.2.1 Audimus ........................................................................................................... 35

    4.2.2 GOP ................................................................................................................. 37

    4.2.3 NGOP .............................................................................................................. 38

    4.2.4 Threshold ......................................................................................................... 39

    4.2.5 GOP for fluent speech ...................................................................................... 40

    4.2.1 Overall score .................................................................................................... 41

    4.2.2 Performance measure ...................................................................................... 41

    4.3 Other classification methods .................................................................. 42

    4.3.1 Likelihood Ratio ................................................................................................ 42

    4.3.2 MFC