Pattern Recognition in Speech and Language Processing

by Wu Chou (Editor), Biing-Hwang Juang (Editor)

Overview

Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field.

Pattern Recognition in Speech and Language Processing offers a systematic, up-to-date presentation of these recent developments. It begins with the fundamentals and recent theoretical advances in pattern recognition, with emphasis on classifier design criteria and optimization procedures. The focus then shifts to the application of these techniques to speech processing, with chapters exploring advances in applying pattern recognition to real speech and audio processing systems. The final section of the book examines topics related to pattern recognition in language processing: topics that represent promising new trends with direct impact on information processing systems for the Web, broadcast news, and other content-rich information resources.

Each self-contained chapter includes figures, tables, diagrams, and references. The collective effort of experts at the forefront of the field, Pattern Recognition in Speech and Language Processing offers in-depth, insightful discussions on new developments and contains a wealth of information integral to the further development of human-machine communications.

Product Details

ISBN-13:	9780849312328
Publisher:	Taylor & Francis
Publication date:	02/26/2003
Series:	Electrical Engineering & Applied Signal Processing Series
Pages:	410
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

Wu Chou, Biing-Hwang Juang

Table of Contents

FUNDAMENTALS AND RECENT ADVANCES IN PATTERN RECOGNITION: Classifier Design Criteria and Discriminant Function Approach in Speech and Language Processing. Minimum Bayes-Risk Automatic Speech Recognition. A Decision Theoretic Formulation for Adaptive and Robust Automatic Speech Recognition. Speech Pattern Recognition Using Neural Networks, Distributed Recognizers, and Decision Fusion.PATTERN RECOGNITION IN SPEECH AND AUDIO PROCESSING: Maximum Mutual Information Training of Hidden Markov Models. Large Vocabulary Speech Recognition Based on Statistical Methods. Toward Spontaneous Speech Recognition. Speaker Authentication. PATTERN RECOGNITION IN LANGUAGE PROCESSING: HMMs Applied Top Language Processing Problems. Statistical Language Models with Embedded Latent Semantic Knowledge. Semantic Information Processing of Spoken Language. Machine Translation Using Stochastic Modeling. Explicit Event Modeling for Topic Detection and Tracking in Broadcast News.