Readings in Speech Recognition
Edited By- Alexander Waibel
- Kai-Fu Lee
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.
Paperback, 680 Pages
Published: May 1990
Imprint: Morgan Kaufmann
ISBN: 978-1-55860-124-6
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
References
Chapter 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
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