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Discrete-Time Processing of Speech Signals
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Wiley
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Preface to the IEEE Edition xvii (2)
Preface xix (4)
Acronyms and Abbreviations xxiii
I Signal Processing Background 3 (96)
1 Propaedeutic 3 (96)
1.0 Preamble 3 (3)
1.0.1 The Purpose of Chapter 1 3 (1)
1.0.2 Please Read This Note on Notation 4 (1)
1.0.3 For People Who Never Read Chapter 5 (1)
1 (and Those Who Do)
1.1 Review of DSP Concepts and Notation 6 (23)
1.1.1 "Normalized Time and Frequency" 6 (3)
1.1.2 Singularity Signals 9 (1)
1.1.3 Energy and Power Signals 9 (1)
1.1.4 Transforms and a Few Related 10 (6)
Concepts
1.1.5 Windows and Frames 16 (4)
1.1.6 Discrete-Time Systems ... 20 (4)
1.1.7 Minimum, Maximum, and Mixed-Phase 24 (5)
Signals and Systems
1.2 Review of Probability and Stochastic 29 (26)
Processes
1.2.1 Probability Spaces 30 (3)
1.2.2 Random Variables 33 (9)
1.2.3 Random Processes 42 (10)
1.2.4 Vector-Valued Random Processes 52 (3)
1.3 Topics in Statistical Pattern 55 (18)
Recognition
1.3.1 Distance Measures 56 (2)
1.3.2 The Euclidean Metric and 58 (5)
"Prewhitening" of Features
1.3.3 Maximum Likelihood Classification 63 (3)
1.3.4 Feature Selection and Probablistic 66 (4)
Separability Measures
1.3.5 Clustering Algorithms 70 (3)
1.4 Information and Entropy 73 (6)
1.4.1 Definitions 73 (4)
1.4.2 Random Sources 77 (1)
1.4.3 Entropy Concepts in Pattern 78 (1)
Recognition
1.5 Phasors and Steady-State Solutions 79 (2)
1.6 Onward to Speech Processing 81 (4)
1.7 Problems 85 (5)
Appendices: Supplemental Bibliography 90 (9)
1.A Example Textbooks on Digital Signal 90 (1)
Processing
1.B Example Textbooks on Stochastic 90 (1)
Processes
1.C Example Textbooks on Statistical 91 (1)
Pattern Recognition
1.D Example Textbooks on Information 91 (1)
Theory
1.E Other Resources on Speech Processing 92 (1)
1.E.1 Textbooks 92 (1)
1.E.2 Edited Paper Collections 92 (1)
1.E.3 Journals 92 (1)
1.E.4 Conference Proceedings 93 (1)
1.F Example Textbooks on Speech and 93 (1)
Hearing Sciences
1.G Other Resources on Artificial Neural 94 (1)
Networks
1.G.1 Textbooks and Monographs 94 (1)
1.G.2 Journals 94 (1)
1.G.3 Conference Proceedings 95 (4)
II Speech Production and Modeling 99 (126)
2 Fundamentals of Speech Science 99 (52)
2.0 Preamble 99 (1)
2.1 Speech Communication 100 (1)
2.2 Anatomy and Physiology of the Speech 101 (14)
Production System
2.2.1 Anatomy 101 (3)
2.2.2 The Role of the Vocal Tract and 104 (6)
Some Elementary Acoustical Analysis
2.2.3 Excitation of the Speech System 110 (5)
and the Physiology of Voicing
2.3 Phonemics and Phonetics 115 (31)
2.3.1 Phonemes Versus Phones 115 (1)
2.3.2 Phonemic and Phonetic Transcription 116 (1)
2.3.3 Phonemic and Phonetic 117 (20)
Classification
2.3.4 Prosodic Features and 137 (9)
Coarticulation
2.4 Conclusions 146 (1)
2.5 Problems 146 (5)
3 Modeling Speech Production 151 (74)
3.0 Preamble 151 (1)
3.1 Acoustic Theory of Speech Production 151 (36)
3.1.1 History 151 (5)
3.1.2 Sound Propagation 156 (3)
3.1.3 Source Excitation Model 159 (7)
3.1.4 Vocal-Tract Modeling 166 (20)
3.1.5 Models for Nasals and Fricatives 186 (1)
3.2 Discrete-Time Modeling 187 (13)
3.2.1 General Discrete-Time Speech Model 187 (5)
3.2.2 A Discrete-Time Filter Model for 192 (5)
Speech Production
3.2.3 Other Speech Models 197 (3)
3.3 Conclusions 200 (1)
3.4 Problems 201 (2)
3.A Single Lossless Tube Analysis 203 (8)
3.A.1 Open and Closed Terminations 203 (3)
3.A.2 Impedance Analysis, T-Network, and 206 (5)
Two-Port Network
3.B Two-Tube Lossless Model of the Vocal 211 (6)
Tract
3.C Fast Discrete-Time Transfer Function 217 (8)
Calculation
III Analysis Techniques 225 (184)
4 Short-Term Processing of Speech 225 (41)
4.1 Introduction 225 (1)
4.2 Short-Term Measures from Long-Term 226 (10)
Concepts
4.2.1 Motivation 226 (1)
4.2.2 "Frames" of Speech 227 (1)
4.2.3 Approach 1 to the Derivation of a 227 (4)
Short-Term Feature and Its Two
Computational Forms
4.2.4 Approach 2 to the Derivation of a 231 (3)
Short-Term Feature and Its Two
Computational Forms
4.2.5 On the Role of "1/N" and Related 234 (2)
Issues
4.3 Example Short-Term Features and 236 (26)
Applications
4.3.1 Short-Term Estimates of 236 (8)
Autocorrelation
4.3.2 Average Magnitude Difference 244 (1)
Function
4.3.3 Zero Crossing Measure 245 (1)
4.3.4 Short-Term Power and Energy 246 (5)
Measures
4.3.5 Short-Term Fourier Analysis 251 (11)
4.4 Conclusions 262 (1)
4.5 Problems 263 (3)
5 Linear Prediction Analysis 266 (86)
5.0 Preamble 266 (1)
5.1 Long-Term LP Analysis by System 267 (13)
Identification
5.1.1 The All-Pole Model 267 (3)
5.1.2 Identification of the Model 270 (10)
5.2 How Good Is the LP Model? 280 (10)
5.2.1 The "Ideal" and "Almost Ideal" 280 (1)
Cases
5.2.2 "Nonideal" Cases 281 (6)
5.2.3 Summary and Further Discussion 287 (3)
5.3 Short-Term LP Analysis 290 (41)
5.3.1 Autocorrelation Method 290 (2)
5.3.2 Covariance Method 292 (4)
5.3.3 Solution Methods 296 (29)
5.3.4 Gain Computation 325 (2)
5.3.5 A Distance Measure for LP 327 (2)
Coefficients
5.3.6 Preemphasis of the Speech Waveform 329 (2)
5.4 Alternative Representations of the LP 331 (2)
Coefficients
5.4.1 The Line Spectrum Pair 331 (2)
5.4.2 Cepstral Parameters 333 (1)
5.5 Applications of LP in Speech Analysis 333 (9)
5.5.1 Pitch Estimation 333 (3)
5.5.2 Formant Estimation and Glottal 336 (6)
Waveform Deconvolution
5.6 Conclusions 342 (1)
5.7 Problems 343 (5)
5.A Proof of Theorem 5.1 348 (2)
5.B The Orthogonality Principle 350 (2)
6 Cepstral Analysis 352 (57)
6.1 Introduction 352 (3)
6.2 "Real" Cepstrum 355 (31)
6.2.1 Long-Term Real Cepstrum 355 (9)
6.2.2 Short-Term Real Cepstrum 364 (2)
6.2.3 Example Applications of the stRC 366 (14)
to Speech Analysis and Recognition
6.2.4 Other Forms and Variations on the 380 (6)
stRC Parameters
6.3 Complex Cepstrum 386 (11)
6.3.1 Long-Term Complex Cepstrum 386 (7)
6.3.2 Short-Term Complex Cepstrum 393 (1)
6.3.3 Example Application of the stCC to 394 (3)
Speech Analysis
6.3.4 Variations on the Complex Cepstrum 397 (1)
6.4 A Critical Analysis of the Cepstrum 397 (4)
and Conclusions
6.5 Problems 401 (8)
IV Coding, Enhancement and Quality Assessment 409 (192)
7 Speech Coding and Synthesis 409 (92)
7.1 Introduction 410 (1)
7.2 Optimum Scalar and Vector Quantization 410 (24)
7.2.1 Scalar Quantization 411 (14)
7.2.2 Vector Quantization 425 (9)
7.3 Waveform Coding 434 (25)
7.3.1 Introduction 434 (1)
7.3.2 Time Domain Waveform Coding 435 (16)
7.3.3 Frequency Domain Waveform Coding 451 (6)
7.3.4 Vector Waveform Quantization 457 (2)
7.4 Vocoders 459 (29)
7.4.1 The Channel Vocoder 460 (2)
7.4.2 The Phase Vocoder 462 (1)
7.4.3 The Cepstral (Homomorphic) Vocoder 462 (7)
7.4.4 Formant Vocoders 469 (2)
7.4.5 Linear Predictive Coding 471 (14)
7.4.6 Vector Quantization of Model 485 (3)
Parameters
7.5 Measuring the Quality of Speech 488 (1)
Compression Techniques
7.6 Conclusions 489 (1)
7.7 Problems 490 (4)
7.A Quadrature Mirror Filters 494 (7)
8 Speech Enhancement 501 (67)
8.1 Introduction 501 (3)
8.2 Classification of Speech Enhancement 504 (2)
Methods
8.3 Short-Term Spectral Amplitude 506 (11)
Techniques
8.3.1 Introduction 506 (1)
8.3.2 Spectral Subtraction 506 (10)
8.3.3 Summary of Short-Term Spectral 516 (1)
Magnitude Methods
8.4 Speech Modeling and Wiener Filtering 517 (11)
8.4.1 Introduction 517 (1)
8.4.2 Iterative Wiener Filtering 517 (4)
8.4.3 Speech Enhancement and All-Pole 521 (3)
Modeling
8.4.4 Sequential Estimation via EM Theory 524 (1)
8.4.5 Constrained Iterative Enhancement 525 (2)
8.4.6 Further Refinements to Iterative 527 (1)
Enhancement
8.4.7 Summary of Speech Modeling and 528 (1)
Wiener Filtering
8.5 Adaptive Noise Canceling 528 (13)
8.5.1 Introduction 528 (2)
8.5.2 ANC Formalities and the LMS 530 (4)
Algorithm
8.5.3 Applications of ANC 534 (7)
8.5.4 Summary of ANC Methods 541 (1)
8.6 Systems Based on Fundamental Frequency 541 (11)
Tracking
8.6.1 Introduction 541 (1)
8.6.2 Single-Channel ANC 542 (3)
8.6.3 Adaptive Comb Filtering 545 (4)
8.6.4 Harmonic Selection 549 (2)
8.6.5 Summary of Systems Based on 551 (1)
Fundamental Frequency Tracking
8.7 Performance Evaluation 552 (4)
8.7.1 Introduction 552 (1)
8.7.2 Enhancement and Perceptual Aspects 552 (2)
of Speech
8.7.3 Speech Enhancement Algorithm 554 (2)
Performance
8.8 Conclusions 556 (1)
8.9 Problems 557 (4)
8.A The INTEL System 561 (4)
8.B Addressing Cross-Talk in Dual-Channel 565 (3)
ANC
9 Speech Quality Assessment 568 (33)
9.1 Introduction 568 (2)
9.1.1 The Need for Quality Assessment 568 (2)
9.1.2 Quality Versus Intelligibility 570 (1)
9.2 Subjective Quality Measures 570 (10)
9.2.1 Intelligibility Tests 572 (3)
9.2.2 Quality Tests 575 (5)
9.3 Objective Quality Measures 580 (13)
9.3.1 Articulation Index 582 (2)
9.3.2 Signal-to-Noise Ratio 584 (3)
9.3.3 Itakura Measure 587 (1)
9.3.4 Other Measures Based on LP Analysis 588 (1)
9.3.5 Weighted-Spectral Slope Measures 589 (1)
9.3.6 Global Objective Measures 590 (1)
9.3.7 Example Applications 591 (2)
9.4 Objective Versus Subjective Measures 593 (2)
9.5 Problems 595 (6)
V Recognition 601 (298)
10 The Speech Recognition Problem 601 (22)
10.1 Introduction 601 (5)
10.1.1 The Dream and the Reality 601 (3)
10.1.2 Discovering Our Ignorance 604 (1)
10.1.3 Circumventing Our Ignorance 605 (1)
10.2 The "Dimensions of Difficulty" 606 (14)
10.2.1 Speaker-Dependent Versus 607 (1)
Speaker-Independent Recognition
10.2.2 Vocabulary Size 607 (1)
10.2.3 Isolated-Word Versus 608 (6)
C
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