The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area

The Challenge of Spoken Language Systems: Research Directions for the Nineties

A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area

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

Aportación a la extracción paramétrica en reconocimiento de voz robusto basada en la aplicación de conocimiento de fonética acústica

This thesis is based on the following hypothesis: the introduction of direct knowledge from the acoustic-phonetic field to the speech recognition problem, especially in the feature extraction step, may constitute a solid base of analysis for the determination of the behavior and capabilities of those systems and their improvement, as well. Most of the complexity of this Ph.D. thesis comes from the different subjects related with the speech processing área. The application of acoustic-phonetic information to the speech recognition research área implies a deep knowledge of both subjects. The research carried out in this work has been divided in two main parts: analysis of the current feature extraction methods and a study of several possible procedures about the incorporation of phonetic-acoustic knowledge to those systems. Abundant recognition and related quality measure results are presented for 50 different parameter extraction models. Details about the real-time implementation on a DSP platform (TMS3230C31-60) of two different parameter extraction models are presented. Finally, a set of computer tools developed for building and testing new speech recognition systems has been produced. Besides, the application of several results from this work can be extended to other speech processing áreas, such as computer assisted language learning, linguistic rehabilitation, etc.---ABSTRACT---La hipótesis en la que se basa el desarrollo de esta tesis, se centra en la suposición de que la aportación de conocimiento directo, proveniente del campo de la fonética acústica, al problema del reconocimiento automático de la voz, en concreto a la etapa de extracción de características, puede constituir una base sólida con la que poder analizar el comportamiento y capacidad de discriminación de dichos sistemas, así como una forma de mejorar sus prestaciones. Parte de la complejidad que presenta esta tesis doctoral, viene motivada por las diferentes disciplinas que están relacionadas con el área de procesamiento de la voz. La aplicación de información fonética-acústica al campo de investigación del reconocimiento del habla requiere un amplio conocimiento de ambas materias. Las investigaciones desarrolladas en este trabajo se han dividido en dos bloques fundamentales: análisis de los métodos actuales de extracción de rasgos fonéticos y un estudio de algunas posibles formas de incorporación de conocimiento fonético-acústico a dichos sistemas. En esta tesis se ofrecen abundantes resultados relativos a tasas de reconocimiento y medidas acerca de la calidad de este proceso, para un total de 50 modelos de extracción de parámetros. Así mismo se incluyen los detalles de la implementación en tiempo real para una plataforma DSP, en concreto TMS320C31-60, de dos diferentes modelos de extracción de rasgos. Además, se ha desarrollado un conjunto de las herramientas informáticas que pueden servir de base para construir y validar de forma sencilla, nuevos sistemas de reconocimiento. La aplicación de algunos de los resultados del trabajo puede extenderse también a otras áreas del tratamiento de la voz, tales como la enseñanza de una segunda lengua, logopedia, etc