Chan Yeuk Chi Joyce. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Previous Work on Code-switching Speech Recognition --- p.2 / Chapter 1.2.1 --- Keyword Spotting Approach --- p.3 / Chapter 1.2.2 --- Translation Approach --- p.4 / Chapter 1.2.3 --- Language Boundary Detection --- p.6 / Chapter 1.3 --- Motivations of Our Work --- p.7 / Chapter 1.4 --- Methodology --- p.8 / Chapter 1.5 --- Thesis Outline --- p.10 / Chapter 1.6 --- References --- p.11 / Chapter Chapter 2 --- Fundamentals of Large Vocabulary Continuous Speech Recognition for Cantonese and English --- p.14 / Chapter 2.1 --- Basic Theory of Speech Recognition --- p.14 / Chapter 2.1.1 --- Feature Extraction --- p.14 / Chapter 2.1.2 --- Maximum a Posteriori (MAP) Probability --- p.15 / Chapter 2.1.3 --- Hidden Markov Model (HMM) --- p.16 / Chapter 2.1.4 --- Statistical Language Modeling --- p.17 / Chapter 2.1.5 --- Search A lgorithm --- p.18 / Chapter 2.2 --- Word Posterior Probability (WPP) --- p.19 / Chapter 2.3 --- Generalized Word Posterior Probability (GWPP) --- p.23 / Chapter 2.4 --- Characteristics of Cantonese --- p.24 / Chapter 2.4.1 --- Cantonese Phonology --- p.24 / Chapter 2.4.2 --- Variation and Change in Pronunciation --- p.27 / Chapter 2.4.3 --- Syllables and Characters in Cantonese --- p.28 / Chapter 2.4.4 --- Spoken Cantonese vs. Written Chinese --- p.28 / Chapter 2.5 --- Characteristics of English --- p.30 / Chapter 2.5.1 --- English Phonology --- p.30 / Chapter 2.5.2 --- English with Cantonese Accents --- p.31 / Chapter 2.6 --- References --- p.32 / Chapter Chapter 3 --- Code-mixing and Code-switching Speech Recognition --- p.35 / Chapter 3.1 --- Introduction --- p.35 / Chapter 3.2 --- Definition --- p.35 / Chapter 3.2.1 --- Monolingual Speech Recognition --- p.35 / Chapter 3.2.2 --- Multilingual Speech Recognition --- p.35 / Chapter 3.2.3 --- Code-mixing and Code-switching --- p.36 / Chapter 3.3 --- Conversation in Hong Kong --- p.38 / Chapter 3.3.1 --- Language Choice of Hong Kong People --- p.38 / Chapter 3.3.2 --- Reasons for Code-mixing in Hong Kong --- p.40 / Chapter 3.3.3 --- How Does Code-mixing Occur? --- p.41 / Chapter 3.4 --- Difficulties for Code-mixing - Specific to Cantonese-English --- p.44 / Chapter 3.4.1 --- Phonetic Differences --- p.45 / Chapter 3.4.2 --- Phonology difference --- p.48 / Chapter 3.4.3 --- Accent and Borrowing --- p.49 / Chapter 3.4.4 --- Lexicon and Grammar --- p.49 / Chapter 3.4.5 --- Lack of Appropriate Speech Corpus --- p.50 / Chapter 3.5 --- References --- p.50 / Chapter Chapter 4 --- Data Collection --- p.53 / Chapter 4.1 --- Data Collection --- p.53 / Chapter 4.1.1 --- Corpus Design --- p.53 / Chapter 4.1.2 --- Recording Setup --- p.59 / Chapter 4.1.3 --- Post-processing of Speech Data --- p.60 / Chapter 4.2 --- A Baseline Database --- p.61 / Chapter 4.2.1 --- Monolingual Spoken Cantonese Speech Data (CUMIX) --- p.61 / Chapter 4.3 --- References --- p.61 / Chapter Chapter 5 --- System Design and Experimental Setup --- p.63 / Chapter 5.1 --- Overview of the Code-mixing Speech Recognizer --- p.63 / Chapter 5.1.1 --- Bilingual Syllable / Word-based Speech Recognizer --- p.63 / Chapter 5.1.2 --- Language Boundary Detection --- p.64 / Chapter 5.1.3 --- Generalized Word Posterior Probability (GWPP) --- p.65 / Chapter 5.2 --- Acoustic Modeling --- p.66 / Chapter 5.2.1 --- Speech Corpus for Training of Acoustic Models --- p.67 / Chapter 5.2.2 --- Features Extraction --- p.69 / Chapter 5.2.3 --- Variability in the Speech Signal --- p.69 / Chapter 5.2.4 --- Language Dependency of the Acoustic Models --- p.71 / Chapter 5.2.5 --- Pronunciation Dictionary --- p.80 / Chapter 5.2.6 --- The Training Process of Acoustic Models --- p.83 / Chapter 5.2.7 --- Decoding and Evaluation --- p.88 / Chapter 5.3 --- Language Modeling --- p.90 / Chapter 5.3.1 --- N-gram Language Model --- p.91 / Chapter 5.3.2 --- Difficulties in Data Collection --- p.91 / Chapter 5.3.3 --- Text Data for Training Language Model --- p.92 / Chapter 5.3.4 --- Training Tools --- p.95 / Chapter 5.3.5 --- Training Procedure --- p.95 / Chapter 5.3.6 --- Evaluation of the Language Models --- p.98 / Chapter 5.4 --- Language Boundary Detection --- p.99 / Chapter 5.4.1 --- Phone-based LBD --- p.100 / Chapter 5.4.2 --- Syllable-based LBD --- p.104 / Chapter 5.4.3 --- LBD Based on Syllable Lattice --- p.106 / Chapter 5.5 --- "Integration of the Acoustic Model Scores, Language Model Scores and Language Boundary Information" --- p.107 / Chapter 5.5.1 --- Integration of Acoustic Model Scores and Language Boundary Information. --- p.107 / Chapter 5.5.2 --- Integration of Modified Acoustic Model Scores and Language Model Scores --- p.109 / Chapter 5.5.3 --- Evaluation Criterion --- p.111 / Chapter 5.6 --- References --- p.112 / Chapter Chapter 6 --- Results and Analysis --- p.118 / Chapter 6.1 --- Speech Data for Development and Evaluation --- p.118 / Chapter 6.1.1 --- Development Data --- p.118 / Chapter 6.1.2 --- Testing Data --- p.118 / Chapter 6.2 --- Performance of Different Acoustic Units --- p.119 / Chapter 6.2.1 --- Analysis of Results --- p.120 / Chapter 6.3 --- Language Boundary Detection --- p.122 / Chapter 6.3.1 --- Phone-based Language Boundary Detection --- p.123 / Chapter 6.3.2 --- Syllable-based Language Boundary Detection (SYL LB) --- p.127 / Chapter 6.3.3 --- Language Boundary Detection Based on Syllable Lattice (BILINGUAL LBD) --- p.129 / Chapter 6.3.4 --- Observations --- p.129 / Chapter 6.4 --- Evaluation of the Language Models --- p.130 / Chapter 6.4.1 --- Character Perplexity --- p.130 / Chapter 6.4.2 --- Phonetic-to-text Conversion Rate --- p.131 / Chapter 6.4.3 --- Observations --- p.131 / Chapter 6.5 --- Character Error Rate --- p.132 / Chapter 6.5.1 --- Without Language Boundary Information --- p.133 / Chapter 6.5.2 --- With Language Boundary Detector SYL LBD --- p.134 / Chapter 6.5.3 --- With Language Boundary Detector BILINGUAL-LBD --- p.136 / Chapter 6.5.4 --- Observations --- p.138 / Chapter 6.6 --- References --- p.141 / Chapter Chapter 7 --- Conclusions and Suggestions for Future Work --- p.143 / Chapter 7.1 --- Conclusion --- p.143 / Chapter 7.1.1 --- Difficulties and Solutions --- p.144 / Chapter 7.2 --- Suggestions for Future Work --- p.149 / Chapter 7.2.1 --- Acoustic Modeling --- p.149 / Chapter 7.2.2 --- Pronunciation Modeling --- p.149 / Chapter 7.2.3 --- Language Modeling --- p.150 / Chapter 7.2.4 --- Speech Data --- p.150 / Chapter 7.2.5 --- Language Boundary Detection --- p.151 / Chapter 7.3 --- References --- p.151 / Appendix A Code-mixing Utterances in Training Set of CUMIX --- p.152 / Appendix B Code-mixing Utterances in Testing Set of CUMIX --- p.175 / Appendix C Usage of Speech Data in CUMIX --- p.202
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_325133 |
| Date | January 2005 |
| Contributors | Chan, Yeuk Chi Joyce., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xv, 204 leaves : ill. ; 30 cm. |
| Coverage | China, Hong Kong |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
Page generated in 0.0028 seconds