Subword lexical modelling for speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 155-160).by Raymond Lau.Ph.D

An acoustic-phonetic approach in automatic Arabic speech recognition

In a large vocabulary speech recognition system the broad phonetic classification technique is used instead of detailed phonetic analysis to overcome the variability in the acoustic realisation of utterances. The broad phonetic description of a word is used as a means of lexical access, where the lexicon is structured into sets of words sharing the same broad phonetic labelling. This approach has been applied to a large vocabulary isolated word Arabic speech recognition system. Statistical studies have been carried out on 10,000 Arabic words (converted to phonemic form) involving different combinations of broad phonetic classes. Some particular features of the Arabic language have been exploited. The results show that vowels represent about 43% of the total number of phonemes. They also show that about 38% of the words can uniquely be represented at this level by using eight broad phonetic classes. When introducing detailed vowel identification the percentage of uniquely specified words rises to 83%. These results suggest that a fully detailed phonetic analysis of the speech signal is perhaps unnecessary. In the adopted word recognition model, the consonants are classified into four broad phonetic classes, while the vowels are described by their phonemic form. A set of 100 words uttered by several speakers has been used to test the performance of the implemented approach. In the implemented recognition model, three procedures have been developed, namely voiced-unvoiced-silence segmentation, vowel detection and identification, and automatic spectral transition detection between phonemes within a word. The accuracy of both the V-UV-S and vowel recognition procedures is almost perfect. A broad phonetic segmentation procedure has been implemented, which exploits information from the above mentioned three procedures. Simple phonological constraints have been used to improve the accuracy of the segmentation process. The resultant sequence of labels are used for lexical access to retrieve the word or a small set of words sharing the same broad phonetic labelling. For the case of having more than one word-candidates, a verification procedure is used to choose the most likely one

Automatic speech recognition of Cantonese-English code-mixing utterances.

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.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Previous Work on Code-switching Speech Recognition --- p.2Chapter 1.2.1 --- Keyword Spotting Approach --- p.3Chapter 1.2.2 --- Translation Approach --- p.4Chapter 1.2.3 --- Language Boundary Detection --- p.6Chapter 1.3 --- Motivations of Our Work --- p.7Chapter 1.4 --- Methodology --- p.8Chapter 1.5 --- Thesis Outline --- p.10Chapter 1.6 --- References --- p.11Chapter Chapter 2 --- Fundamentals of Large Vocabulary Continuous Speech Recognition for Cantonese and English --- p.14Chapter 2.1 --- Basic Theory of Speech Recognition --- p.14Chapter 2.1.1 --- Feature Extraction --- p.14Chapter 2.1.2 --- Maximum a Posteriori (MAP) Probability --- p.15Chapter 2.1.3 --- Hidden Markov Model (HMM) --- p.16Chapter 2.1.4 --- Statistical Language Modeling --- p.17Chapter 2.1.5 --- Search A lgorithm --- p.18Chapter 2.2 --- Word Posterior Probability (WPP) --- p.19Chapter 2.3 --- Generalized Word Posterior Probability (GWPP) --- p.23Chapter 2.4 --- Characteristics of Cantonese --- p.24Chapter 2.4.1 --- Cantonese Phonology --- p.24Chapter 2.4.2 --- Variation and Change in Pronunciation --- p.27Chapter 2.4.3 --- Syllables and Characters in Cantonese --- p.28Chapter 2.4.4 --- Spoken Cantonese vs. Written Chinese --- p.28Chapter 2.5 --- Characteristics of English --- p.30Chapter 2.5.1 --- English Phonology --- p.30Chapter 2.5.2 --- English with Cantonese Accents --- p.31Chapter 2.6 --- References --- p.32Chapter Chapter 3 --- Code-mixing and Code-switching Speech Recognition --- p.35Chapter 3.1 --- Introduction --- p.35Chapter 3.2 --- Definition --- p.35Chapter 3.2.1 --- Monolingual Speech Recognition --- p.35Chapter 3.2.2 --- Multilingual Speech Recognition --- p.35Chapter 3.2.3 --- Code-mixing and Code-switching --- p.36Chapter 3.3 --- Conversation in Hong Kong --- p.38Chapter 3.3.1 --- Language Choice of Hong Kong People --- p.38Chapter 3.3.2 --- Reasons for Code-mixing in Hong Kong --- p.40Chapter 3.3.3 --- How Does Code-mixing Occur? --- p.41Chapter 3.4 --- Difficulties for Code-mixing - Specific to Cantonese-English --- p.44Chapter 3.4.1 --- Phonetic Differences --- p.45Chapter 3.4.2 --- Phonology difference --- p.48Chapter 3.4.3 --- Accent and Borrowing --- p.49Chapter 3.4.4 --- Lexicon and Grammar --- p.49Chapter 3.4.5 --- Lack of Appropriate Speech Corpus --- p.50Chapter 3.5 --- References --- p.50Chapter Chapter 4 --- Data Collection --- p.53Chapter 4.1 --- Data Collection --- p.53Chapter 4.1.1 --- Corpus Design --- p.53Chapter 4.1.2 --- Recording Setup --- p.59Chapter 4.1.3 --- Post-processing of Speech Data --- p.60Chapter 4.2 --- A Baseline Database --- p.61Chapter 4.2.1 --- Monolingual Spoken Cantonese Speech Data (CUMIX) --- p.61Chapter 4.3 --- References --- p.61Chapter Chapter 5 --- System Design and Experimental Setup --- p.63Chapter 5.1 --- Overview of the Code-mixing Speech Recognizer --- p.63Chapter 5.1.1 --- Bilingual Syllable / Word-based Speech Recognizer --- p.63Chapter 5.1.2 --- Language Boundary Detection --- p.64Chapter 5.1.3 --- Generalized Word Posterior Probability (GWPP) --- p.65Chapter 5.2 --- Acoustic Modeling --- p.66Chapter 5.2.1 --- Speech Corpus for Training of Acoustic Models --- p.67Chapter 5.2.2 --- Features Extraction --- p.69Chapter 5.2.3 --- Variability in the Speech Signal --- p.69Chapter 5.2.4 --- Language Dependency of the Acoustic Models --- p.71Chapter 5.2.5 --- Pronunciation Dictionary --- p.80Chapter 5.2.6 --- The Training Process of Acoustic Models --- p.83Chapter 5.2.7 --- Decoding and Evaluation --- p.88Chapter 5.3 --- Language Modeling --- p.90Chapter 5.3.1 --- N-gram Language Model --- p.91Chapter 5.3.2 --- Difficulties in Data Collection --- p.91Chapter 5.3.3 --- Text Data for Training Language Model --- p.92Chapter 5.3.4 --- Training Tools --- p.95Chapter 5.3.5 --- Training Procedure --- p.95Chapter 5.3.6 --- Evaluation of the Language Models --- p.98Chapter 5.4 --- Language Boundary Detection --- p.99Chapter 5.4.1 --- Phone-based LBD --- p.100Chapter 5.4.2 --- Syllable-based LBD --- p.104Chapter 5.4.3 --- LBD Based on Syllable Lattice --- p.106Chapter 5.5 --- "Integration of the Acoustic Model Scores, Language Model Scores and Language Boundary Information" --- p.107Chapter 5.5.1 --- Integration of Acoustic Model Scores and Language Boundary Information. --- p.107Chapter 5.5.2 --- Integration of Modified Acoustic Model Scores and Language Model Scores --- p.109Chapter 5.5.3 --- Evaluation Criterion --- p.111Chapter 5.6 --- References --- p.112Chapter Chapter 6 --- Results and Analysis --- p.118Chapter 6.1 --- Speech Data for Development and Evaluation --- p.118Chapter 6.1.1 --- Development Data --- p.118Chapter 6.1.2 --- Testing Data --- p.118Chapter 6.2 --- Performance of Different Acoustic Units --- p.119Chapter 6.2.1 --- Analysis of Results --- p.120Chapter 6.3 --- Language Boundary Detection --- p.122Chapter 6.3.1 --- Phone-based Language Boundary Detection --- p.123Chapter 6.3.2 --- Syllable-based Language Boundary Detection (SYL LB) --- p.127Chapter 6.3.3 --- Language Boundary Detection Based on Syllable Lattice (BILINGUAL LBD) --- p.129Chapter 6.3.4 --- Observations --- p.129Chapter 6.4 --- Evaluation of the Language Models --- p.130Chapter 6.4.1 --- Character Perplexity --- p.130Chapter 6.4.2 --- Phonetic-to-text Conversion Rate --- p.131Chapter 6.4.3 --- Observations --- p.131Chapter 6.5 --- Character Error Rate --- p.132Chapter 6.5.1 --- Without Language Boundary Information --- p.133Chapter 6.5.2 --- With Language Boundary Detector SYL LBD --- p.134Chapter 6.5.3 --- With Language Boundary Detector BILINGUAL-LBD --- p.136Chapter 6.5.4 --- Observations --- p.138Chapter 6.6 --- References --- p.141Chapter Chapter 7 --- Conclusions and Suggestions for Future Work --- p.143Chapter 7.1 --- Conclusion --- p.143Chapter 7.1.1 --- Difficulties and Solutions --- p.144Chapter 7.2 --- Suggestions for Future Work --- p.149Chapter 7.2.1 --- Acoustic Modeling --- p.149Chapter 7.2.2 --- Pronunciation Modeling --- p.149Chapter 7.2.3 --- Language Modeling --- p.150Chapter 7.2.4 --- Speech Data --- p.150Chapter 7.2.5 --- Language Boundary Detection --- p.151Chapter 7.3 --- References --- p.151Appendix A Code-mixing Utterances in Training Set of CUMIX --- p.152Appendix B Code-mixing Utterances in Testing Set of CUMIX --- p.175Appendix C Usage of Speech Data in CUMIX --- p.20

Two level continuous speech recognition using demisyllable-based HMM word spotting

This paper describes a two level Spanish Continuous Speech Recognition System based on Demisyllable HMM modelling, word-spotting and finite-state lexical and syntactic knowledge. The first level, the word level, is based on a spotting algorithm which takes as input the unknown utterance, the HMM of the reference demisyllable and the lexical knowledge in terms of a finite-state network. The output of the word level is a lattice of word hypothesis [1]. The second level, the phrase level, searches in a time-synchronous procedure the best sentence that end at each time instant. It takes as input the word lattice and the syntactic knowledge in terms of a finite-state network, giving as output the best legal sentence. The proposal two-level system was tested recognizing the integers from 0 to 1000 in a speaker independent approach. We get a word accuracy of 93,2% with a sentence accuracy of 84. 5%. Keywords: Speech Recognition, Hidden Markov Model, Fuzzy Training, Demisyllable, Word-spotting, Multiple Hypothesis, Finite State Networks.Peer ReviewedPostprint (published version

Two Level Continuous Speech Recognition Using Demisyllable-Based HMM Word Spotting

Peer Reviewe

Two Level Continuous Speech Recognition Using Demisyllable-Based HMM Word Spotting

Peer ReviewedPostprint (published version

Two level continuous speech recognition using demisyllable-based HMM word spotting

This paper describes a two level Spanish Continuous Speech Recognition System based on Demisyllable HMM modelling, word-spotting and finite-state lexical and syntactic knowledge. The first level, the word level, is based on a spotting algorithm which takes as input the unknown utterance, the HMM of the reference demisyllable and the lexical knowledge in terms of a finite-state network. The output of the word level is a lattice of word hypothesis [1]. The second level, the phrase level, searches in a time-synchronous procedure the best sentence that end at each time instant. It takes as input the word lattice and the syntactic knowledge in terms of a finite-state network, giving as output the best legal sentence. The proposal two-level system was tested recognizing the integers from 0 to 1000 in a speaker independent approach. We get a word accuracy of 93,2% with a sentence accuracy of 84. 5%. Keywords: Speech Recognition, Hidden Markov Model, Fuzzy Training, Demisyllable, Word-spotting, Multiple Hypothesis, Finite State Networks.Peer Reviewe

Two Level Continuous Speech Recognition Using Demisyllable-Based HMM Word Spotting

Peer Reviewe