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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.

The advent of powerful computing devices and ubiquitous multimedia information distribution has triggered a wave of new approaches in pattern recognition. This book provides a systematic and timely presentation of the fundamentals and recent developments in the field of speech and language processing. It begins with the basics and recent theoretical advances, emphasizing classifier design criteria and optimization procedures. Discussions then move to topics in speech recognition and, finally, to language processing. Numerous examples of practical applications and real system descriptions make this a truly comprehensive treatment for an important and rapidly growing field.

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Minimum Classification Error (MSE) Approach in Pattern Recognition, Wu Chou
Minimum Bayes-Risk Methods in Automatic Speech Recognition, Vaibhava Goel and William Byrne
A Decision Theoretic Formulation for Adaptive and Robust Automatic Speech Recognition, Qiang Huo
Speech Pattern Recognition Using Neural Networks, Shigeru Katagiri
Large Vocabulary Speech Recognition Based on Statistical Methods, Jean-Luc Gauvain
Toward Spontaneous Speech Recognition and Understanding, Sadaoki Furui
Speaker Authentication, Qi Li and Biing-Hwang Juang
HMMs for Language Processing Problems, Richard M. Schwartz and John Makhoul
Statistical Language Models with Embedded Latent Semantic Knowledge, Jerome R. Bellegarda
Semantic Information Processing of Spoken Language - How May I Help You?sm, A.L. Gorin, A. Abella, T. Alonso, G . Riccardi, and J.H. Wright
Machine Translation Using Statistical Modeling, H. Ney and F.J. Och
Modeling Topics for Detection and Tracking, James Allen

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