published_or_final_version / abstract / toc / Computer Science / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

by Choy Chi Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 111-115). / Text in English; abstract also in Chinese. / by Choy Chi Yan. / ABSTRACT --- p.I / ACKNOWLEDGMENTS --- p.III / TABLE OF CONTENTS --- p.IV / LIST OF FIGURES --- p.VII / LIST OF TABLES --- p.VIII / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Speech Recognition --- p.1 / Chapter 1.2 --- Development of Speech Recognisers --- p.4 / Chapter 1.3 --- Speech Recognition for Chinese Language --- p.5 / Chapter 1.4 --- Objectives of the thesis --- p.6 / Chapter 1.5 --- Thesis Structure --- p.7 / Chapter 2. --- PHONOLOGICAL AND ACOUSTICAL PROPERTIES OF MANDARIN CHINESE --- p.9 / Chapter 2.1 --- Characteristics of Mandarin Chinese --- p.9 / Chapter 2.1.1 --- Syllabic Structures --- p.10 / Chapter 2.1.2 --- Lexical Tones --- p.11 / Chapter 2.2 --- Basic Phonetic Units for Mandarin Chinese --- p.14 / Chapter 2.2.1 --- Tonal Syllables and Base Syllables --- p.14 / Chapter 2.2.2 --- Initial-Finals --- p.14 / Chapter 2.2.3 --- Phones --- p.16 / Chapter 2.2.4 --- Preme-Core-Finals and Preme-Tonemes --- p.17 / Chapter 2.2.5 --- Summary-The phonological hierarchy of Mandarin Syllables --- p.19 / Chapter 3. --- HIDDEN MARKOV MODELS --- p.20 / Chapter 3.1 --- Introduction --- p.20 / Chapter 3.1.1 --- Speech Data --- p.20 / Chapter 3.1.2 --- Fundamental of HMMs --- p.21 / Chapter 3.2 --- Using Hidden Markov Models for Speech Recognition --- p.22 / Chapter 3.2.1 --- Likelihood of the state sequence of speech observations --- p.22 / Chapter 3.2.2 --- The Recognition Problem --- p.24 / Chapter 3.3 --- Output Probability Distributions --- p.25 / Chapter 3.4 --- Model Training --- p.26 / Chapter 3.4.1 --- State Sequence Estimation --- p.26 / Chapter 3.4.2 --- Gaussian Mixture Models --- p.29 / Chapter 3.4.3 --- Parameter Estimation --- p.30 / Chapter 3.5 --- Speech Recognition and Viterbi Decoding --- p.31 / Chapter 3.6 --- Summary --- p.32 / Chapter 4. --- LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION FOR MANDARIN CHINESE --- p.33 / Chapter 4.1 --- Introduction --- p.33 / Chapter 4.2 --- Large Vocabulary Mandarin Chinese Recognition System --- p.34 / Chapter 4.2.1 --- Overall Architecture for the Speech Recogniser --- p.34 / Chapter 4.2.2 --- Signal Representation and Features --- p.36 / Chapter 4.2.3 --- Subword Unit Models Based on HMMs --- p.39 / Chapter 4.2.4 --- Training of Subword Units --- p.42 / Chapter 4.2.5 --- Language Model (LM) --- p.43 / Chapter 4.2.6 --- "Transcriptions, Word Networks and Dictionaries for LVCSR System" --- p.44 / Chapter 4.2.7 --- Viterbi Decoding --- p.47 / Chapter 4.2.8 --- Performance Analysis --- p.48 / Chapter 4.3 --- Experiments --- p.48 / Chapter 4.3.1 --- Tasks --- p.48 / Chapter 4.3.2 --- Speech Database --- p.49 / Chapter 4.3.3 --- Baseline Experimental Results --- p.51 / Chapter 4.4 --- Context Dependency in Speech --- p.52 / Chapter 4.4.1 --- Introduction --- p.52 / Chapter 4.4.2 --- Context Dependent Phonetic Models --- p.53 / Chapter 4.4.3 --- Word Boundaries and Word network for context-dependent HMMs --- p.54 / Chapter 4.4.4 --- Recognition Results Using Cross-Syllable Context-Dependent Units --- p.56 / Chapter 4.5 --- Tree-Based Clustering --- p.58 / Chapter 4.5.1 --- Introduction --- p.58 / Chapter 4.5.2 --- Decision Tree Based Clustering --- p.59 / Chapter 4.5.3 --- The Question Sets --- p.61 / Chapter 4.5.4 --- Convergence Condition --- p.61 / Chapter 4.4.5 --- The Final Results --- p.63 / Chapter 4.6 --- Conclusions --- p.65 / Chapter 5. --- APPLICATION1 ISOLATED WORD RECOGNITION FOR MANDARIN CHINESE --- p.67 / Chapter 5.1 --- Introduction --- p.67 / Chapter 5.2 --- Isolated Word Recogniser --- p.68 / Chapter 5.2.1 --- System Description --- p.68 / Chapter 5.2.2 --- Experimental Results --- p.70 / Chapter 5.3 --- Discussions and Conclusions --- p.71 / Chapter 6. --- APPLICATION2 SUBWORD UNITS FOR A MANDARIN KEYWORD SPOTTING SYSTEM --- p.74 / Chapter 6.1 --- INTRODUCTION --- p.74 / Chapter 6.2 --- RECOGNITION SYSTEM DESCRIPTION --- p.75 / Chapter 6.2.1 --- Overall Architecture and Recognition Network for the keyword Spotters --- p.75 / Chapter 6.2.2 --- Signal Representation and Features --- p.76 / Chapter 6.2.3 --- Keyword Models --- p.76 / Chapter 6.2.4 --- Filler Models --- p.77 / Chapter 6.2.5 --- Language Modeling and Search --- p.78 / Chapter 6.3 --- EXPERIMENTS --- p.78 / Chapter 6.3.1 --- Tasks --- p.78 / Chapter 6.3.2 --- Speech Database --- p.79 / Chapter 6.3.3 --- Performance Measures --- p.80 / Chapter 6.3.4 --- Details of Different Word-spotters --- p.80 / Chapter 6.3.5 --- General Filler Models --- p.81 / Chapter 6.4 --- EXPERIMENTAL RESULTS --- p.83 / Chapter 6.5 --- CONCLUSIONS --- p.84 / Chapter 7. --- CONCLUSIONS --- p.87 / Chapter 7.1 --- Review of the Work --- p.87 / Chapter 7.1.1 --- Large Vocabulary Continuous Speech Recognition for Mandarin Chinese --- p.87 / Chapter 7.1.2 --- Isolated Word Recognition for a Stock Inquiry Application --- p.88 / Chapter 7.1.3 --- Keyword Spotting for Mandarin Chinese --- p.89 / Chapter 7.2 --- Suggestions for Further Work --- p.89 / Chapter 7.3 --- Conclusion --- p.91 / APPENDIX --- p.92 / BIBLIOGRAPHY --- p.111

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy