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数字语音信号处理,已经matlab的使用,在看书的过程中,学习语音 信号处理 以及matlab知识
E.S. Gopi Digital Speech Processing USing Matlab 空 Springer E.s. Gopi Electronics and communication Engineering National Institute of Technology amI ISSN1860-4862 issn 1860-4870(electronic ISBN978-81-322-1676-6 ISBN978-81-322-1677-3( eBook) DOI10.1007/978-81-322-1677-3 Springer new delhi heidelberg new York dordrecht London Library of Congress Control Number: 2013953196 o Springer India 2014 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publishers location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein Printed on acid-free paper SpringerispartofSpringerScience+businessMedia(www.springer.com) Dedicated to my wife g. viji, my son A.G. Vasig and my daughter A G. Desna Preface The most of the applications of digital speech processing deal with speech or speaker pattern recognition. To understand the practical implementation of the peech or speaker recognition techniques, there is the need to understand the concepts of digital speech processing and the pattern recognition. This book aims in giving the balanced treatment of both the concepts. This book deals with speech processing concepts like speech production model, speech feature extraction, speech compression, etc, and the basic pattern recognition concepts applied to speech signals like PCA, LDA, ICA, sVM, HMM, GMM, BPN, KsOm, etc. The book is written such that it is suitable for the beginners who are doing basic research in digital speech processing. All the topics covered in this book are llustrated using Matlab in almost all the topics for better understanding Acknowledgments I would like to thank Profs. s. Soundararajan Director, NITT, Trichy) M. Chidambaram (IITm, Chennai), K.M. M. Prabhu(IITm, Chennai), P Palanisamy P. Somaskandan, B. Venkataramani, and s. Raghavan (nitt, Trichy) for their support. I would also like to thank those who helped directly or indirectly in bringing out this book successfully. Special thanks to my parents Mr E Sankara Subbu and mrses. meena Thanks E. S. G Contents 1 Pattern Recognition for Speech Detection 1.1 Introduction 1.2 Back-propagation Neural Network 1. 2.1 Back-propagation Algorithm 12.2 ann Illustration 7 1. 3 Support Vector Machine 1.3.1 Dual Problem to Solve(1.25)-(1.28) 14 1.3.2"Kernel-Trick'for Nonlinear Separation in SVM 1.3.3 Illustration for Support Vector Machine 16 1.4.1 Baum-Welch Technique to Obtain the Unknown Parameters in HMM 26 1.4.2 Steps to Compute the unknown Parameters of HMM Using expectation-Maximization algorithm 28 1. 4.3 Viterbi Algorithm to Compute the Generatin Probability of the arbitrary speech h segment 1.4.4 Isolated Word recognition Using HMM 30 1.4.5 Alignment Method to Model hmm 1. 4.6 Illustration of hidden markov model 31 1.5 Gaussian Mixture Model 37 1. 5.1 Steps Involved to Model GMm using Expectation-Maximization algorithm 39 1.5.2 Isolated Word Recognition Using GMM 39 1.5.3 Illustration of gmm 40 1.6 Unsupervised Learning System 43 1.6.1 Need for Unsupervised Learning System 43 1.6.2 k-Means algorithm 43 1.6.3 Illustration of k-Means algorithm 1.6.4 Fuzzy k-Means Algorithm 44 1.6.5 Steps Involved in Fuzzy k-Means Clustering 46 1.6.6 Illustration of fuzzy k-Means algorithm 46 1.6.7 Kohonen Self-Organizing Map 48 1. 6. 8 Illustration of KSom 51 Xll Contents 1.7 Dimensionality Reduction Techniques 1.7.1 Principal Component Analysis 53 1.7.2 Illustration of PCA Using 2D to ID Conversion 54 1.7.3 Illustration of pCa 54 1.7.4 Linear Discriminant Analysis 55 1.7.5 Small Sample size problem in LDA 57 1.7.6 Null-Space LDa 1.7.7 Kernel lDa 58 1.7. 8 Kerne -Trick to Execute LDA in the higher-Dimensional space 59 1.7.9 Illustration of dimensionality reduction USing LDa 1. 8 Independent Component Analysis 64 1. 8.1 Solving ICA Bases Using Kurtosis Measurement 66 1.8.2 Steps to Obtain the ICA Bases 1.8.3 Illustration of dimensionality Reduction Using ICa 2 Speech Production Model 2.1 Introduction..·· 2.2 1-D Sound waves 74 2.2.1 Physics on Sound Wave Travelling Through the Tube with Uniform Cross-Sectional area a 74 2.2.2 Solution to(2.9) and(2. 18) 76 2.3 Vocal Tract Model as the Cascade Connections of identical ength Tubes with different cross-Sections 78 2. 4 Modelling the Vocal Tract from the Speech Signal 2.4.1 Autocorrelation method 82 2.4.2 Auto Covariance method 2.5 Lattice Structure to Obtain Excitation source for the Typical Speech Signal 88 2.5.1 Computation of Lattice Co-efficient from lPc co-efficients 3 Feature Extraction of the Speech Signal 3. 1 Endpoint Detection 3.2 Dynamic Time Warping 6 3.3 Linear Predictive Co-efficients 3.4 Poles of the vocal tract 103 3.5 Reflection Co-efficients 105 36 Log Area ratio∴. 3.7 Cepstrum 105 3.8 Line Spectral Frequencies 3.9 Mel-Frequency Cepstral Co-efficients 113 3.9.1 Gibbs phenomenon 117 3.9.2 Discrete Cosine Transformation l18 Contents 3.10 Spectrogram 120 3.10. 1 Time Resolution Versus frequency Resolution in spectrogram 124 3.11 Discrete Wavelet Transformation 124 3.12 Pitch Frequency estimation 126 3. 12.1 Autocorrelation Approach 126 3. 12.2 Homomorphic Filtering Approach 127 3.13 Formant Frequency Estimation 129 3.13.1 Formant Extraction Using Vocal Tract Model 129 3. 13.2 Formant Extraction Using Homomorphic Filtering.. 132 Speech Compression 135 4.1 Uniform Quantization 135 4.2 Nonuniform Quantization 136 4.3 Adaptive Quantization 139 4. 4 Differential Pulse Code modulation 140 4.4.1 Illustrations of the Prediction of Speech Signal using lpc 140 4.5 Code-Excited Linear prediction 142 4. 5. 1 Estimation of the delay Constant D 145 4.5.2 Estimation of the gain Constants g1 and g2 146 4.6 Assessment of the Quality of the Compressed Speech signal ..150 Appendix A: Constrained Optimization Using Lagrangian Techniques 151 Appendix B: Expectation-Maximization Algorithm ..157 Appendix C: Diagonalization of the matrix ..161 ppendix D: Condition Number Appendix e: Spectral Flatness 169 Appendix F: Functional Blocks of the Vocal Tract and the ear 175 about the author 177 about the book Index ...181

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