Pronunciation modeling for Cantonese speech recognition.

Kam Patgi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaf 103).Abstracts in English and Chinese.Chapter Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Automatic Speech Recognition --- p.1Chapter 1.2 --- Pronunciation Modeling in ASR --- p.2Chapter 1.3 --- Obj ectives of the Thesis --- p.5Chapter 1.4 --- Thesis Outline --- p.5Reference --- p.7Chapter Chapter 2. --- The Cantonese Dialect --- p.9Chapter 2.1 --- Cantonese - A Typical Chinese Dialect --- p.10Chapter 2.1.1 --- Cantonese Phonology --- p.11Chapter 2.1.2 --- Cantonese Phonetics --- p.12Chapter 2.2 --- Pronunciation Variation in Cantonese --- p.13Chapter 2.2.1 --- Phone Change and Sound Change --- p.14Chapter 2.2.2 --- Notation for Different Sound Units --- p.16Chapter 2.3 --- Summary --- p.17Reference --- p.18Chapter Chapter 3. --- Large-Vocabulary Continuous Speech Recognition for Cantonese --- p.19Chapter 3.1 --- Feature Representation of the Speech Signal --- p.20Chapter 3.2 --- Probabilistic Framework of ASR --- p.20Chapter 3.3 --- Hidden Markov Model for Acoustic Modeling --- p.21Chapter 3.4 --- Pronunciation Lexicon --- p.25Chapter 3.5 --- Statistical Language Model --- p.25Chapter 3.6 --- Decoding --- p.26Chapter 3.7 --- The Baseline Cantonese LVCSR System --- p.26Chapter 3.7.1 --- System Architecture --- p.26Chapter 3.7.2 --- Speech Databases --- p.28Chapter 3.8 --- Summary --- p.29Reference --- p.30Chapter Chapter 4. --- Pronunciation Model --- p.32Chapter 4.1 --- Pronunciation Modeling at Different Levels --- p.33Chapter 4.2 --- Phone-level pronunciation model and its Application --- p.35Chapter 4.2.1 --- IF Confusion Matrix (CM) --- p.35Chapter 4.2.2 --- Decision Tree Pronunciation Model (DTPM) --- p.38Chapter 4.2.3 --- Refinement of Confusion Matrix --- p.41Chapter 4.3 --- Summary --- p.43References --- p.44Chapter Chapter 5. --- Pronunciation Modeling at Lexical Level --- p.45Chapter 5.1 --- Construction of PVD --- p.46Chapter 5.2 --- PVD Pruning by Word Unigram --- p.48Chapter 5.3 --- Recognition Experiments --- p.49Chapter 5.3.1 --- Experiment 1 ´ؤPronunciation Modeling in LVCSR --- p.49Chapter 5.3.2 --- Experiment 2 ´ؤ Pronunciation Modeling in Domain Specific task --- p.58Chapter 5.3.3 --- Experiment 3 ´ؤ PVD Pruning by Word Unigram --- p.62Chapter 5.4 --- Summary --- p.63Reference --- p.64Chapter Chapter 6. --- Pronunciation Modeling at Acoustic Model Level --- p.66Chapter 6.1 --- Hierarchy of HMM --- p.67Chapter 6.2 --- Sharing of Mixture Components --- p.68Chapter 6.3 --- Adaptation of Mixture Components --- p.70Chapter 6.4 --- Combination of Mixture Component Sharing and Adaptation --- p.74Chapter 6.5 --- Recognition Experiments --- p.78Chapter 6.6 --- Result Analysis --- p.80Chapter 6.6.1 --- Performance of Sharing Mixture Components --- p.81Chapter 6.6.2 --- Performance of Mixture Component Adaptation --- p.84Chapter 6.7 --- Summary --- p.85Reference --- p.87Chapter Chapter 7. --- Pronunciation Modeling at Decoding Level --- p.88Chapter 7.1 --- Search Process in Cantonese LVCSR --- p.88Chapter 7.2 --- Model-Level Search Space Expansion --- p.90Chapter 7.3 --- State-Level Output Probability Modification --- p.92Chapter 7.4 --- Recognition Experiments --- p.93Chapter 7.4.1 --- Experiment 1 ´ؤModel-Level Search Space Expansion --- p.93Chapter 7.4.2 --- Experiment 2 ´ؤ State-Level Output Probability Modification …… --- p.94Chapter 7.5 --- Summary --- p.96Reference --- p.97Chapter Chapter 8. --- Conclusions and Suggestions for Future Work --- p.98Chapter 8.1 --- Conclusions --- p.98Chapter 8.2 --- Suggestions for Future Work --- p.100Reference --- p.103Appendix I Base Syllable Table --- p.104Appendix II Cantonese Initials and Finals --- p.105Appendix III IF confusion matrix --- p.106Appendix IV Phonetic Question Set --- p.112Appendix V CDDT and PCDT --- p.11

Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method

The automatic identification and analysis of pronunciation errors, known as Mispronunciation Detection and Diagnosis (MDD) plays a crucial role in Computer Aided Pronunciation Learning (CAPL) tools such as Second-Language (L2) learning or speech therapy applications. Existing MDD methods relying on analysing phonemes can only detect categorical errors of phonemes that have an adequate amount of training data to be modelled. With the unpredictable nature of the pronunciation errors of non-native or disordered speakers and the scarcity of training datasets, it is unfeasible to model all types of mispronunciations. Moreover, phoneme-level MDD approaches have a limited ability to provide detailed diagnostic information about the error made. In this paper, we propose a low-level MDD approach based on the detection of speech attribute features. Speech attribute features break down phoneme production into elementary components that are directly related to the articulatory system leading to more formative feedback to the learner. We further propose a multi-label variant of the Connectionist Temporal Classification (CTC) approach to jointly model the non-mutually exclusive speech attributes using a single model. The pre-trained wav2vec2 model was employed as a core model for the speech attribute detector. The proposed method was applied to L2 speech corpora collected from English learners from different native languages. The proposed speech attribute MDD method was further compared to the traditional phoneme-level MDD and achieved a significantly lower False Acceptance Rate (FAR), False Rejection Rate (FRR), and Diagnostic Error Rate (DER) over all speech attributes compared to the phoneme-level equivalent

Acoustic Modelling for Under-Resourced Languages

Automatic speech recognition systems have so far been developed only for very few languages out of the 4,000-7,000 existing ones. In this thesis we examine methods to rapidly create acoustic models in new, possibly under-resourced languages, in a time and cost effective manner. For this we examine the use of multilingual models, the application of articulatory features across languages, and the automatic discovery of word-like units in unwritten languages

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

End-to-end Lip-reading: A Preliminary Study

Deep lip-reading is the combination of the domains of computer vision and natural language processing. It uses deep neural networks to extract speech from silent videos. Most works in lip-reading use a multi staged training approach due to the complex nature of the task. A single stage, end-to-end, unified training approach, which is an ideal of machine learning, is also the goal in lip-reading. However, pure end-to-end systems have not yet been able to perform as good as non-end-to-end systems. Some exceptions to this are the very recent Temporal Convolutional Network (TCN) based architectures. This work lays out preliminary study of deep lip-reading, with a special focus on various end-to-end approaches. The research aims to test whether a purely end-to-end approach is justifiable for a task as complex as deep lip-reading. To achieve this, the meaning of pure end-to-end is first defined and several lip-reading systems that follow the definition are analysed. The system that most closely matches the definition is then adapted for pure end-to-end experiments. Four main contributions have been made: i) An analysis of 9 different end-to-end deep lip-reading systems, ii) Creation and public release of a pipeline1 to adapt sentence level Lipreading Sentences in the Wild 3 (LRS3) dataset into word level, iii) Pure end-to-end training of a TCN based network and evaluation on LRS3 word-level dataset as a proof of concept, iv) a public online portal2 to analyse visemes and experiment live end-to-end lip-reading inference. The study is able to verify that pure end-to-end is a sensible approach and an achievable goal for deep machine lip-reading

Automated Testing of Speech-to-Speech Machine Translation in Telecom Networks

Globalisoituvassa maailmassa kyky kommunikoida kielimuurien yli käy yhä tärkeämmäksi. Kielten opiskelu on työlästä ja siksi halutaan kehittää automaattisia konekäännösjärjestelmiä. Ericsson on kehittänyt prototyypin nimeltä Real-Time Interpretation System (RTIS), joka toimii mobiiliverkossa ja kääntää matkailuun liittyviä fraaseja puhemuodossa kahden kielen välillä. Nykyisten konekäännösjärjestelmien suorituskyky on suhteellisen huono ja siksi testauksella on suuri merkitys järjestelmien suunnittelussa. Testauksen tarkoituksena on varmistaa, että järjestelmä säilyttää käännösekvivalenssin sekä puhekäännösjärjestelmän tapauksessa myös riittävän puheenlaadun. Luotettavimmin testaus voidaan suorittaa ihmisten antamiin arviointeihin perustuen, mutta tällaisen testauksen kustannukset ovat suuria ja tulokset subjektiivisia. Tässä työssä suunniteltiin ja analysoitiin automatisoitu testiympäristö Real-Time Interpretation System -käännösprototyypille. Tavoitteina oli tutkia, voidaanko testaus suorittaa automatisoidusti ja pystytäänkö todellinen, käyttäjän havaitsema käännösten laatu mittaamaan automatisoidun testauksen keinoin. Tulokset osoittavat että mobiiliverkoissa puheenlaadun testaukseen käytetyt menetelmät eivät ole optimaalisesti sovellettavissa konekäännösten testaukseen. Nykytuntemuksen mukaan ihmisten suorittama arviointi on ainoa luotettava tapa mitata käännösekvivalenssia ja puheen ymmärrettävyyttä. Konekäännösten testauksen automatisointi vaatii lisää tutkimusta, jota ennen subjektiivinen arviointi tulisi säilyttää ensisijaisena testausmenetelmänä RTIS-testauksessa.In the globalizing world, the ability to communicate over language barriers is increasingly important. Learning languages is laborious, which is why there is a strong desire to develop automatic machine translation applications. Ericsson has developed a speech-to-speech translation prototype called the Real-Time Interpretation System (RTIS). The service runs in a mobile network and translates travel phrases between two languages in speech format. The state-of-the-art machine translation systems suffer from a relatively poor performance and therefore evaluation plays a big role in machine translation development. The purpose of evaluation is to ensure the system preserves the translational equivalence, and in case of a speech-to-speech system, the speech quality. The evaluation is most reliably done by human judges. However, human-conducted evaluation is costly and subjective. In this thesis, a test environment for Ericsson Real-Time Interpretation System prototype is designed and analyzed. The goals are to investigate if the RTIS verification can be conducted automatically, and if the test environment can truthfully measure the end-to-end performance of the system. The results conclude that methods used in end-to-end speech quality verification in mobile networks can not be optimally adapted for machine translation evaluation. With current knowledge, human-conducted evaluation is the only method that can truthfully measure translational equivalence and the speech intelligibility. Automating machine translation evaluation needs further research, until which human-conducted evaluation should remain the preferred method in RTIS verification

A study on reusing resources of speech synthesis for closely-related languages

This thesis describes research on building a text-to-speech (TTS) framework that can accommodate the lack of linguistic information of under-resource languages by using existing resources from another language. It describes the adaptation process required when such limited resource is used. The main natural languages involved in this research are Malay and Iban language. The thesis includes a study on grapheme to phoneme mapping and the substitution of phonemes. A set of substitution matrices is presented which show the phoneme confusion in term of perception among respondents. The experiments conducted study the intelligibility as well as perception based on context of utterances. The study on the phonetic prosody is then presented and compared to the Klatt duration model. This is to find the similarities of cross language duration model if one exists. Then a comparative study of Iban native speaker with an Iban polyglot TTS using Malay resources is presented. This is to confirm that the prosody of Malay can be used to generate Iban synthesised speech. The central hypothesis of this thesis is that by using a closely-related language resource, a natural sounding speech can be produced. The aim of this research was to show that by sticking to the indigenous language characteristics, it is possible to build a polyglot synthesised speech system even with insufficient speech resources