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Automatic Speech and Speaker Recognition: Advanced Topics / Edition 1

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Overview

Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.
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Editorial Reviews

Booknews
According to these 42 researchers from AT&T, IBM, Microsoft, and academia, the day is coming when computer users who yell at their computers may get an earful back. Speech and speech recognition is a highly provocative field in computer science, and this "first of its kind" text gathers together the seminal research now being done in signal processing, algorithms, architecture, and hardware, including the adoption of statistical pattern recognition paradigm, and the hidden Markov modeling framework. The discussions provide technology overviews, discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations, addressing issues related to flexibility and robustness, and the theoretical and practical issues of search. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Table of Contents

Contributors
Preface
1 An Overview of Automatic Speech Recognition 1
2 An Overview of Speaker Recognition Technology 31
3 Maximum Mutual Information Estimation of Hidden Markov Models 57
4 Bayesian Adaptive Learning and Map Estimation of HMM 83
5 Statistical and Discriminative Methods for Speech Recognition 109
6 Context Dependent Vector Quantization for Speech Recognition 133
7 Hidden Markov Network for Precise Acoustic Modeling 159
8 From HMMS to Segment Models: Stochastic Modeling for CSR 185
9 Voice Identification Using Nonparametric Density Matching 211
10 The Use of Recurrent Networks in Continuous Speech Recognition 233
11 Hybrid Connectionist Models for Continuous Speech Recognition 259
12 Automatic Generation of Detailed Pronunciation Lexicons 285
13 Word Spotting - Extracting Partial Information from Continuous Utterances 303
14 Spectral Dynamics for Speech Recognition Under Adverse Conditions 331
15 Signal Processing for Robust Speech Recognition 357
16 Dynamic Programming Search: From Digit Strings to Large Vocabulary Word Graphs 385
17 Fast Matching Techniques 413
18 Multiple-Pass Search Strategies 429
19 Issues in Practical Large Vocabulary Isolated Word Recognition: The IBM Tangora System 457
20 From Sphinx-II to Whisper: Making Speech Recognition Usable 481
Index 509
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