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Readings in Speech Recognition


Author: Alexander WaibelKai-Fu Lee

Publisher: Morgan Kaufmann

Publish Date: 25th December 1990

ISBN-13: 9780080515847

Pages: 680

Language: English



After more than two decades of research activity, speech recognition has begun to live up to its promise as a practical technology and interest in the field is growing dramatically. Readings in Speech Recognition provides a collection of seminal papers that have influenced or redirected the field and that illustrate the central insights that have emerged over the years.The editors provide an introduction to the field, its concerns and research problems. Subsequent chapters are devoted to the main schools of thought and design philosophies that have motivated different approaches to speech recognition system design. Each chapter includes an introduction to the papers that highlights the major insights or needs that have motivated an approach to a problem and describes the commonalities and differences of that approach to others in the book.

Table of Contents

Readings in Speech Recognition Edited by Alex Waibel and Kai-Fu Lee Chapter 1 Why Study Speech Recognition? Introduction Dimensions of Difficulty in Speech Recognition The Chapters of this Book Further Study ReferencesChapter 2 Problems and Opportunities Introduction 2.1 Speech Recognition by Machine: A Review D. R. Reddy 2.2 The Value of Speech Recognition Systems W. A. Lea Chapter 3 Speech Analysis Introduction References 3.1 Digital Representations of Speech Signals R. W. Schafer and L. R. Rabiner 3.2 Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences S. B. Davis and P. Mermelstein 3.3 Vector Quantization R. M. Gray 3.4 A Joint Synchrony/Mean-Rate Model of Auditory Speech Processing S. Seneff Chapter 4 Template-Based Approaches Introduction References 4.1 Isolated and Connected Word Recognition Theory and Selected Applications L. R. Rabiner and S. E. Levinson 4.2 Minimum Prediction Residual Principle Applied to Speech Recognition F. Itakura 4.3 Dynamic Programming Algorithm Optimization for Spoken Word Recognition H. Sakoe and S. Chiba 4.4 Speaker-Independent Recognition of Isolated Words Using Clustering Techniques L. R. Rabiner, S. E. Levinson, A. E. Rosenberg, and J. G. Wilpon 4.5 Two-Level DP-Matching(emA Dynamic Programming-Based Pattern Matching Algorithm for Connected Word Recognition H. Sakoe 4.6 The Use of a One-Stage Dynamic Programming Algorithm for Connected Word Recognition H. Ney Chapter 5 Knowledge-Based Approaches Introduction References 5.1 The Use of Speech Knowledge in Automatic Speech Recognition V. W. Zue 5.2 Performing Fine Phonetic Distinctions: Templates versus Features R. A. Cole, R. M. Stern, and M. J. Lasry 5.3 Recognition of Speaker-Dependent Continuous Speech with KEAL G. Mercier, D. Bigorgne, L. Miclet, L. Le Guennec, and M. Querre 5.4 The Hearsay-II Speech Understanding System: A Tutorial L. D. Erman and V. R. Lesser 5.5 Learning and Plan Refinement in a Knowledge-Based System for Automatic Speech Recognition R. De Mori, L. Lam, and M. Gilloux Chapter 6 Stochastic Approaches Introduction References 6.1 A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition L. R. Rabiner 6.2 Stochastic Modeling for Automatic Speech Understanding J. K. Baker 6.3 A Maximum Likeihood Approach to Continuous Speech Recognition L. R. Bahl, F. Jelinek, and R. L. Mercer 6.4 High Performance Connected Digit Recognition Using Hidden Markov Models L. R. Rabiner, J. G. Wilpon, and F. K. Soong 6.5 Speech Recognition With Continuous-Parameter Hidden Markov Models L. R. Bahl, P. F. Brown, P. V. de Souza, and R. L. Mercer 6.6 Semi-Continuous Hidden Markov Models for Speech Signals X. D. Huang and M. A. Jack 6.7 Context-Dependent Phonetic Hidden Markov Models for Speaker-Independent Continuous Speech Recognition K-F. Lee 6.8 A Stochastic Segment Model for Phoneme-Based Continuous Speech Recognition S. Roucos and M. O. Dunham Chapter 7 Connectionist Approaches Introduction References 7.1 Review of Neural Networks for Speech Recognition R. P. Lippmann 7.2 Phoneme Recognition Using Time-Delay Neural Networks A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K. J. Lang 7.3 Consonant Recognition by Modular Construction of Large Phonemic Time-Delay Neural Networks A. Waibel, H. Sawai, and K. Shikano 7.4 Learned Phonetic Discrimination Using Connectionist Networks R. L. Watrous, L. Shastri, and A. H. Waibel 7.5 The Neural” Phonetic Typewriter T. Kohonen 7.6 Shift-Tolerant LVQ and Hybrid LVQ-HMM for Phoneme Recognition E. McDermott, H. Iwamida, S. Katagiri, and Y. Tohkura 7.7 Speaker-Independent Word Recognition Using Dynamic Programming Neural Networks H. Sakoe, R. Isotani, K. Yoshida, K. Iso, and T. Watanabe 7.8 Speaker-Independent Word Recognition Using a Neural Prediction Model K. Iso and T. Watanabe Chapter 8 Language Processing for Speech Recognition Introduction References 8.1 Self-Organized Language Modeling for Speech Recognition F. Jelinek 8.2 A Tree-Based Statistical Language Model for Natural Language Speech Recognition L. R. Bahl, P. F. Brown, P. V. de Souza, and R. L. Mercer 8.3 Modification of Earley’s Algorithm for Speech Recognition A. Paeseler 8.4 Language Processing for Speech Understanding W. A. Woods 8.5 Prosodic Knowledge Sources for Word Hypothesization in a Continuous Speech Recognition System A. Waibel 8.6 High Level Knowledge Sources in Usable Speech Recognition Systems S. R. Young, A. G. Hauptmann, W. H. Ward, E. T. Smith, and P. Werner Chapter 9 Systems Introduction References 9.1 Review of the ARPA Speech Understanding Project D. H. Klatt 9.2 The Harpy Speech Understanding System B. Lowerre 9.3 The Development of an Experimental Discrete Dictation Recognizer F. Jelinek 9.4 BYBLOS: The BBN Continuous Speech Recognition System Y. L. Chow, M. O. Dunham, O. A. Kimball, M. A. Krasner, G. F. Kubala, J. Makhoul, P. J. Price, S. Roucos, and R. M. Schwartz 9.5 An Overview of the SPHINX Speech Recognition System K-F. Lee, H-W. Hon, and R. Reddy 9.6 ATR HMM-LR Continuous Speech Recognition System T. Hanazawa, K. Kita, S. Nakamura, T. Kawabata, and K. Shikano 9.7 A Word Hypothesizer for a Large Vocabulary Continuous Speech Understanding System L. Fissore, P. Laface, G. Micca, and R. Pieraccini Index Credits