Towards an automatic speech recognition system for use by deaf students in lectures

According to the Royal National Institute for Deaf people there are nearly 7.5 million hearing-impaired people in Great Britain. Human-operated machine transcription systems, such as Palantype, achieve low word error rates in real-time. The disadvantage is that they are very expensive to use because of the difficulty in training operators, making them impractical for everyday use in higher education. Existing automatic speech recognition systems also achieve low word error rates, the disadvantages being that they work for read speech in a restricted domain. Moving a system to a new domain requires a large amount of relevant data, for training acoustic and language models. The adopted solution makes use of an existing continuous speech phoneme recognition system as a front-end to a word recognition sub-system. The subsystem generates a lattice of word hypotheses using dynamic programming with robust parameter estimation obtained using evolutionary programming. Sentence hypotheses are obtained by parsing the word lattice using a beam search and contributing knowledge consisting of anti-grammar rules, that check the syntactic incorrectness’ of word sequences, and word frequency information. On an unseen spontaneous lecture taken from the Lund Corpus and using a dictionary containing "2637 words, the system achieved 815% words correct with 15% simulated phoneme error, and 73.1% words correct with 25% simulated phoneme error. The system was also evaluated on 113 Wall Street Journal sentences. The achievements of the work are a domain independent method, using the anti- grammar, to reduce the word lattice search space whilst allowing normal spontaneous English to be spoken; a system designed to allow integration with new sources of knowledge, such as semantics or prosody, providing a test-bench for determining the impact of different knowledge upon word lattice parsing without the need for the underlying speech recognition hardware; the robustness of the word lattice generation using parameters that withstand changes in vocabulary and domain

Identification and correction of speech repairs in the context of an automatic speech recognition system

Recent advances in automatic speech recognition systems for read (dictated) speech have led researchers to confront the problem of recognising more spontaneous speech. A number of problems, such as disfluencies, appear when read speech is replaced with spontaneous speech. In this work we deal specifically with what we class as speech-repairs. Most disfluency processes deal with speech-repairs at the sentence level. This is too late in the process of speech understanding. Speech recognition systems have problems recognising speech containing speech-repairs. The approach taken in this work is to deal with speech-repairs during the recognition process. Through an analysis of spontaneous speech the grammatical structure of speech- repairs was identified as a possible source of information. It is this grammatical structure, along with some pattern matching to eliminate false positives, that is used in the approach taken in this work. These repair structures are identified within a word lattice and when found result in a SKIP being added to the lattice to allow the reparandum of the repair to be ignored during the hypothesis generation process. Word fragment information is included using a sub-word pattern matching process and cue phrases are also identified within the lattice and used in the repair detection process. These simple, yet effective, techniques have proved very successful in identifying and correcting speech-repairs in a number of evaluations performed on a speech recognition system incorporating the repair procedure. On an un-seen spontaneous lecture taken from the Durham corpus, using a dictionary of 2,275 words and phoneme corruption of 15%, the system achieved a correction recall rate of 72% and a correction precision rate of 75%.The achievements of the project include the automatic detection and correction of speech-repairs, including word fragments and cue phrases, in the sub-section of an automatic speech recognition system processing spontaneous speech

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