Pattern Recognition in Speech and Language

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.

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
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Pattern Recognition in Speech and Language Processing

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