Language modeling for speech recognition of spoken Cantonese.

Yeung, Yu Ting.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 84-93).Abstracts in English and Chinese.Acknowledgement --- p.iiiAbstract --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Cantonese Speech Recognition --- p.3Chapter 1.2 --- Objectives --- p.4Chapter 1.3 --- Thesis Outline --- p.5Chapter 2 --- Fundamentals of Large Vocabulary Continuous Speech Recognition --- p.7Chapter 2.1 --- Problem Formulation --- p.7Chapter 2.2 --- Feature Extraction --- p.8Chapter 2.3 --- Acoustic Models --- p.9Chapter 2.4 --- Decoding --- p.10Chapter 2.5 --- Statistical Language Modeling --- p.12Chapter 2.5.1 --- N-gram Language Models --- p.12Chapter 2.5.2 --- N-gram Smoothing --- p.13Chapter 2.5.3 --- Complexity of Language Model --- p.15Chapter 2.5.4 --- Class-based Langauge Model --- p.16Chapter 2.5.5 --- Language Model Pruning --- p.17Chapter 2.6 --- Performance Evaluation --- p.18Chapter 3 --- The Cantonese Dialect --- p.19Chapter 3.1 --- Phonology of Cantonese --- p.19Chapter 3.2 --- Orthographic Representation of Cantonese --- p.22Chapter 3.3 --- Classification of Cantonese speech --- p.25Chapter 3.4 --- Cantonese-English Code-mixing --- p.27Chapter 4 --- Rule-based Translation Method --- p.29Chapter 4.1 --- Motivations --- p.29Chapter 4.2 --- Transformation-based Learning --- p.30Chapter 4.2.1 --- Algorithm Overview --- p.30Chapter 4.2.2 --- Learning of Translation Rules --- p.32Chapter 4.3 --- Performance Evaluation --- p.35Chapter 4.3.1 --- The Learnt Translation Rules --- p.35Chapter 4.3.2 --- Evaluation of the Rules --- p.37Chapter 4.3.3 --- Analysis of the Rules --- p.37Chapter 4.4 --- Preparation of Training Data for Language Modeling --- p.41Chapter 4.5 --- Discussion --- p.43Chapter 5 --- Language Modeling for Cantonese --- p.44Chapter 5.1 --- Training Data --- p.44Chapter 5.1.1 --- Text Corpora --- p.44Chapter 5.1.2 --- Preparation of Formal Cantonese Text Data --- p.45Chapter 5.2 --- Training of Language Models --- p.46Chapter 5.2.1 --- Language Models for Standard Chinese --- p.46Chapter 5.2.2 --- Language Models for Formal Cantonese --- p.46Chapter 5.2.3 --- Language models for Colloquial Cantonese --- p.47Chapter 5.3 --- Evaluation of Language Models --- p.48Chapter 5.3.1 --- Speech Corpora for Evaluation --- p.48Chapter 5.3.2 --- Perplexities of Formal Cantonese Language Models --- p.49Chapter 5.3.3 --- Perplexities of Colloquial Cantonese Language Models --- p.51Chapter 5.4 --- Speech Recognition Experiments --- p.53Chapter 5.4.1 --- Speech Corpora --- p.53Chapter 5.4.2 --- Experimental Setup --- p.54Chapter 5.4.3 --- Results on Formal Cantonese Models --- p.55Chapter 5.4.4 --- Results on Colloquial Cantonese Models --- p.56Chapter 5.5 --- Analysis of Results --- p.58Chapter 5.6 --- Discussion --- p.59Chapter 5.6.1 --- Cantonese Language Modeling --- p.59Chapter 5.6.2 --- Interpolated Language Models --- p.59Chapter 5.6.3 --- Class-based Language Models --- p.60Chapter 6 --- Towards Language Modeling of Code-mixing Speech --- p.61Chapter 6.1 --- Data Collection --- p.61Chapter 6.1.1 --- Data Collection --- p.62Chapter 6.1.2 --- Filtering of Collected Data --- p.63Chapter 6.1.3 --- Processing of Collected Data --- p.63Chapter 6.2 --- Clustering of Chinese and English Words --- p.64Chapter 6.3 --- Language Modeling for Code-mixing Speech --- p.64Chapter 6.3.1 --- Language Models from Collected Data --- p.64Chapter 6.3.2 --- Class-based Language Models --- p.66Chapter 6.3.3 --- Performance Evaluation of Code-mixing Language Models --- p.67Chapter 6.4 --- Speech Recognition Experiments with Code-mixing Language Models --- p.69Chapter 6.4.1 --- Experimental Setup --- p.69Chapter 6.4.2 --- Monolingual Cantonese Recognition --- p.70Chapter 6.4.3 --- Code-mixing Speech Recognition --- p.72Chapter 6.5 --- Discussion --- p.74Chapter 6.5.1 --- Data Collection from the Internet --- p.74Chapter 6.5.2 --- Speech Recognition of Code-mixing Speech --- p.75Chapter 7 --- Conclusions and Future Work --- p.77Chapter 7.1 --- Conclusions --- p.77Chapter 7.1.1 --- Rule-based Translation Method --- p.77Chapter 7.1.2 --- Cantonese Language Modeling --- p.78Chapter 7.1.3 --- Code-mixing Language Modeling --- p.78Chapter 7.2 --- Future Works --- p.79Chapter 7.2.1 --- Rule-based Translation --- p.79Chapter 7.2.2 --- Training data --- p.80Chapter 7.2.3 --- Code-mixing speech --- p.80Chapter A --- Equation Derivation --- p.82Chapter A.l --- Relationship between Average Mutual Information and Perplexity --- p.82Bibliography --- p.8