Farouk Application of Wavelets in Speech Processing

2. Auflage 2018
ISBN: 978-3-319-69002-5
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 96 Seiten

Reihe: SpringerBriefs in Speech Technology

This new edition provides an updated and enhanced survey on employing wavelets analysis in an array of applications of speech processing. The author presents updated developments in topics such as; speech enhancement, noise suppression, spectral analysis of speech signal, speech quality assessment, speech recognition, forensics by Speech, and emotion recognition from speech. The new edition also features  a new chapter on scalogram analysis of speech. Moreover, in this edition, each chapter is restructured as such; that it becomes self contained, and can be read separately. Each chapter surveys the literature in a topic such that the use of wavelets in the work is explained and experimental results of proposed method are then discussed. Illustrative figures are also added to explain the methodology of each work.

Mohamed Hesham Farouk El-Sayed is a full professor with the Engineering Math & Physics Department within the Faculty of Engineering at Cairo University. He obtained his B.Sc. in electronics and telecommunications engineering with honors on 1982, another B.Sc. in physics on 1985 and M.Sc. in engineering physics on 1989 all from Cairo university. He received his Ph.D. in Engineering Physics from Cairo University on 1993. He is the author and coauthor of several published papers on the application of wavelets in the analysis of speech and on modeling of speech production in reputable periodicals and conferences since 1993. He is also the author of Application of Wavelets in Speech Processing (Springer 2014). M. Hesham has been actively involved in several national R&D projects on speech recognition since 1982.

Autoren/Hrsg.

Farouk, Mohamed Hesham

Inhaltsverzeichnis

1;Dedication;6
2;Preface;7
2.1;Organization of the Book;7
2.2; Acknowledgment;8
3;Abbreviations;12
4;Contents;9
5;Chapter 1: Introduction;14
5.1;1.1 History and Definition of Speech Processing;14
5.2;1.2 Applications of Speech Processing;15
5.3;1.3 Recent Progress in Speech Processing;15
5.4;1.4 Wavelet Analysis as an Efficient Tool for Speech Processing;16
5.5;References;17
6;Chapter 2: Speech Production and Perception;18
6.1;2.1 Speech Production Process;18
6.2;2.2 Classification of Speech Sounds;19
6.3;2.3 Speech Production Modeling;20
6.4;2.4 Speech Perception Modeling;21
6.5;2.5 Intelligibility and Speech Quality Measures;22
6.6;References;23
7;Chapter 3: Wavelets, Wavelet Filters, and Wavelet Transforms;24
7.1;3.1 Short-Time Fourier Transform (STFT);24
7.2;3.2 Multiresolution Analysis and Wavelet Transform;25
7.3;3.3 Wavelets and Bank of Filters;27
7.4;3.4 Wavelet Families;28
7.5;3.5 Wavelet Packets;29
7.6;3.6 Undecimated Wavelet Transform;31
7.7;3.7 The Continuous Wavelet Transform (CWT);31
7.8;3.8 Wavelet Scalogram;32
7.9;3.9 Empirical Wavelets;32
7.10;References;33
8;Chapter 4: Spectral Analysis of Speech Signal and Pitch Estimation;35
8.1;4.1 Spectral Analysis;35
8.2;4.2 Formant Tracking and Estimation;36
8.3;4.3 Pitch Estimation;37
8.4;References;39
9;Chapter 5: Speech Detection and Separation;41
9.1;5.1 Voice Activity Detection;41
9.2;5.2 Segmentation of Speech Signal;42
9.3;5.3 Source Separation of Speech;43
9.4;References;45
10;Chapter 6: Speech Enhancement and Noise Suppression;46
10.1;6.1 Thresholding Schemes;47
10.2;6.2 Thresholding on Wavelet Packet Coefficients;48
10.3;6.3 Enhancement on Multitaper Spectrum;49
10.4;References;50
11;Chapter 7: Speech Recognition;52
11.1;7.1 Signal Enhancement and Noise Cancellation for Robust Recognition;52
11.2;7.2 Wavelet-Based Features for Better Recognition;53
11.3;7.3 Hybrid Approach;54
11.4;7.4 Wavelet as an Activation Function for Neural Networks in ASR;55
11.5;References;56
12;Chapter 8: Speaker Identification;58
12.1;8.1 Wavelet-Based Features for Speaker Identification;59
12.2;8.2 Hybrid Feature Sets for Speaker Identification;60
12.3;References;60
13;Chapter 9: Emotion Recognition from Speech;62
13.1;9.1 Wavelet-Based Features for Emotion Recognition;62
13.2;9.2 Combined Feature Set for Better Emotion Recognition;64
13.3;9.3 WNN for Emotion Recognition;65
13.4;References;65
14;Chapter 10: Speech Coding, Synthesis, and Compression;67
14.1;10.1 Speech Synthesis;67
14.2;10.2 Speech Coding and Compression;68
14.3;10.3 Real-Time Implementation of DWT-Based Speech Compression;68
14.4;References;69
15;Chapter 11: Speech Quality Assessment;71
15.1;11.1 Wavelet-Packet Analysis;71
15.2;11.2 Discrete Wavelet Transform;73
15.3;References;74
16;Chapter 12: Scalogram and Nonlinear Analysis of Speech;75
16.1;12.1 Wavelet-Based Nonlinear Features;75
16.2;12.2 Wavelet Scalogram Analysis;76
16.3;12.3 Nonlinear and Chaotic Components in Speech Signal;77
16.4;References;79
17;Chapter 13: Steganography, Forensics, and Security of Speech Signal;81
17.1;13.1 Secure Communication of Speech;81
17.2;13.2 Watermarking of Speech;83
17.3;13.3 Watermarking in Sparse Representation;83
17.4;13.4 Forensic Analysis of Speech;84
17.5;References;85
18;Chapter 14: Clinical Diagnosis and Assessment of Speech Pathology;87
18.1;References;89
19;Index;91