This thesis introduces a general method for using information at the utterance level and across utterances for automatic speech recognition. The method involves classification of utterances into types. Using constraints at the utterance level via this classification method allows information sources to be exploited which cannot necessarily be used directly for word recognition. The classification power of three sources of information is investigated: the language model in the speech recogniser, dialogue context and intonation. The method is applied to a challenging task: the recognition of spontaneous dialogue speech. The results show success in automatic utterance type classification, and subsequent word error rate reduction over a baseline system, when all three information sources are probabilistically combined.

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published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

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M.Com. (Informatics) / Please refer to full text to view abstract

M.Com. / Please refer to full text to view abstract

Yau Pui Yuk. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 147-152). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Speech Recognition --- p.2 / Chapter 1.2.1 --- How Speech Recognition Works --- p.3 / Chapter 1.2.2 --- Types of Speech Recognition Tasks --- p.4 / Chapter 1.2.3 --- Variabilities in Speech 一 a Challenge for Speech Recog- nition --- p.6 / Chapter 1.3 --- Performance Prediction of Speech Recognition Task --- p.7 / Chapter 1.4 --- Thesis Goals --- p.9 / Chapter 1.5 --- Thesis Organization --- p.10 / Chapter 2 --- Background --- p.11 / Chapter 2.1 --- The Acoustic-phonetic Approach --- p.12 / Chapter 2.1.1 --- Prediction based on the Degree of Mismatch --- p.12 / Chapter 2.1.2 --- Prediction based on Acoustic Similarity --- p.13 / Chapter 2.1.3 --- Prediction based on Between-Word Distance --- p.14 / Chapter 2.2 --- The Lexical Approach --- p.16 / Chapter 2.2.1 --- Perplexity --- p.16 / Chapter 2.2.2 --- SMR-perplexity --- p.17 / Chapter 2.3 --- The Combined Acoustic-phonetic and Lexical Approach --- p.18 / Chapter 2.3.1 --- Speech Decoder Entropy (SDE) --- p.19 / Chapter 2.3.2 --- Ideal Speech Decoding Difficulty (ISDD) --- p.20 / Chapter 2.4 --- Chapter Summary --- p.23 / Chapter 3 --- Components for Predicting the Performance of Speech Recog- nition Task --- p.24 / Chapter 3.1 --- Components of Speech Recognizer --- p.25 / Chapter 3.2 --- Word Similarity Measure --- p.27 / Chapter 3.2.1 --- Universal Phoneme Symbol (UPS) --- p.30 / Chapter 3.2.2 --- Definition of Phonetic Distance --- p.31 / Chapter 3.2.3 --- Definition of Word Pair Phonetic Distance --- p.45 / Chapter 3.2.4 --- Definition of Word Similarity Measure --- p.47 / Chapter 3.3 --- Word Occurrence Measure --- p.62 / Chapter 3.4 --- Chapter Summary --- p.64 / Chapter 4 --- Formulation of Recognition Error Predictive Index (REPI) --- p.65 / Chapter 4.1 --- Formulation of Recognition Error Predictive Index (REPI) --- p.66 / Chapter 4.2 --- Characteristics of Recognition Error Predictive Index (REPI) --- p.74 / Chapter 4.2.1 --- Weakness of Ideal Speech Decoding Difficulty (ISDD) --- p.75 / Chapter 4.2.2 --- Advantages of Recognition Error Predictive Index (REPI) --- p.79 / Chapter 4.3 --- Chapter Summary --- p.82 / Chapter 5 --- Experimental Design and Setup --- p.83 / Chapter 5.1 --- Objectives --- p.83 / Chapter 5.2 --- Experiments Preparation --- p.84 / Chapter 5.2.1 --- Speech Corpus and Speech Recognizers --- p.85 / Chapter 5.2.2 --- Speech Recognition Tasks --- p.93 / Chapter 5.2.3 --- Evaluation Criterion --- p.98 / Chapter 5.3 --- Experiment Categories and their Setup --- p.99 / Chapter 5.3.1 --- Experiment Category 1 一 Investigating and comparing the overall prediction performance of the two predictive indices --- p.102 / Chapter 5.3.2 --- Experiment Category 2 一 Comparing the applicability of the word similarity measures of the two predictive indices on predicting the recognition performance --- p.104 / Chapter 5.3.3 --- Experiment Category 3 - Comparing the applicability of the formulation method of the two predictive indices on predicting the recognition performance --- p.107 / Chapter 5.3.4 --- Experiment Category 4 一 Comparing the performance of different phonetic distance definitions --- p.109 / Chapter 5.4 --- Chapter Summary --- p.111 / Chapter 6 --- Experimental Results and Analysis --- p.112 / Chapter 6.1 --- Experimental Results and Analysis --- p.113 / Chapter 6.1.1 --- Experiment Category 1 - Investigating and comparing the overall prediction performance of the two predictive indices --- p.113 / Chapter 6.1.2 --- Experiment Category 2- Comparing the applicability of the word similarity measures of the two predictive indices on predicting the recognition performance --- p.117 / Chapter 6.1.3 --- Experiment Category 3 一 Comparing the applicability of the formulation method of the two predictive indices on predicting the recognition performance --- p.124 / Chapter 6.1.4 --- Experiment Category 4 - Comparing the performance of different phonetic distance definitions --- p.131 / Chapter 6.2 --- Experimental Summary --- p.137 / Chapter 6.3 --- Chapter Summary --- p.141 / Chapter 7 --- Conclusions --- p.142 / Chapter 7.1 --- Contributions --- p.144 / Chapter 7.2 --- Future Directions --- p.145 / Bibliography --- p.147 / Chapter A --- Table of Universal Phoneme Symbol --- p.153 / Chapter B --- Vocabulary Lists --- p.157 / Chapter C --- Experimental Results of Two-words Speech Recognition Tasks --- p.171 / Chapter D --- Experimental Results of Three-words Speech Recognition Tasks --- p.180 / Chapter E --- Significance Testing --- p.190 / Chapter E.1 --- Procedures of Significance Testing --- p.190 / Chapter E.2 --- Results of the Significance Testing --- p.191 / Chapter E.2.1 --- Experiment Category 1 --- p.191 / Chapter E.2.2 --- Experiment Category 2 --- p.192 / Chapter E.2.3 --- Experiment Category 3 --- p.194 / Chapter E.2.4 --- Experiment Category 4 --- p.196 / Chapter F --- Linear Regression Models --- p.197