This landmark book offers a balanced discussion of both the mathematical theory of digital speech signal processing and critical contemporary applications. Authors John R. Deller, John H. L. Hansen and John G. Proakis provide a comprehensive view of all major modern speech processing areas: speech production physiology and modeling, signal analysis techniques, coding, enhancement, quality assessment, and recognition. You will learn the principles needed to understand advanced technologies in speech processing, from speech coding for communications systems to biomedical applications of speech analysis and recognition. Ideal for selfstudy or as a course text, this farreaching reference book offers an extensive historical context for concepts under discussion, endofchapter problems, and practical algorithms. Table of Contents Preface to the IEEE Edition Preface Acronyms and Abbreviations Signal Processing Background Propaedeutic Preamble  The Purpose of Chapter 1
 Please Read This Note on Notation
 For People Who Never Read Chapter 1 (and Those Who Do)
Review of DSP Concepts and Notation  "Normalized Time and Frequency"
 Singularity Signals
 Energy and Power Signals
 Transforms and a Few Related Concepts
 Windows and Frames
 DiscreteTime Systems
 Minimum, Maximum, and MixedPhase Signals and Systems
Review of Probability and Stochastic Processes  Probability Spaces
 Random Variables
 Random Processes
 VectorValued Random Processes
Topics in Statistical Pattern Recognition  Distance Measures
 The Euclidean Metric and "Prewhitening" of Features
 Maximum Likelihood Classification
 Feature Selection and Probablistic Separability Measures
 Clustering Algorithms
Information and Entropy  Definitions
 Random Sources
 Entropy Concepts in Pattern Recognition
Phasors and SteadyState Solutions Onward to Speech Processing Problems Appendices: Supplemental Bibliography Example Textbooks on Digital Signal Processing Example Textbooks on Stochastic Processes Example Textbooks on Statistical Pattern Recognition Example Textbooks on Information Theory Other Resources on Speech Processing  Textbooks
 Edited Paper Collections
 Journals
 Conference Proceedings
Example Textbooks on Speech and Hearing Sciences Other Resources on Artificial Neural Networks  Textbooks and Monographs
 Journals
 Conference Proceedings
Speech Production and Modeling Fundamentals of Speech Science Preamble Speech Communication Anatomy and Physiology of the Speech Production System  Anatomy
 The Role of the Vocal Tract and Some Elementary Acoustical Analysis
 Excitation of the Speech System and the Physiology of Voicing
Phonemics and Phonetics  Phonemes Versus Phones
 Phonemic and Phonetic Transcription
 Phonemic and Phonetic Classification
 Prosodic Features and Coarticulation
Conclusions Problems Modeling Speech Production Preamble Acoustic Theory of Speech Production  History
 Sound Propagation
 Source Excitation Model
 VocalTract Modeling
 Models for Nasals and Fricatives
DiscreteTime Modeling  General DiscreteTime Speech Model
 A DiscreteTime Filter Model for Speech Production
 Other Speech Models
Conclusions Problems Single Lossless Tube Analysis  Open and Closed Terminations
 Impedance Analysis, TNetwork, and TwoPort Network
TwoTube Lossless Model of the Vocal Tract Fast DiscreteTime Transfer Function Calculation Analysis Techniques ShortTerm Processing of Speech Introduction ShortTerm Measures from LongTerm Concepts  Motivation
 "Frames" of Speech
 Approach 1 to the Derivation of a ShortTerm Feature and Its Two Computational Forms
 Approach 2 to the Derivation of a ShortTerm Feature and Its Two Computational Forms
 On the Role of "1/N" and Related Issues
Example ShortTerm Features and Applications  ShortTerm Estimates of Autocorrelation
 Average Magnitude Difference Function
 Zero Crossing Measure
 ShortTerm Power and Energy Measures
 ShortTerm Fourier Analysis
Conclusions Problems Linear Prediction Analysis Preamble LongTerm LP Analysis by System Identification  The AllPole Model
 Identification of the Model
How Good Is the LP Model?  The "Ideal" and "Almost Ideal" Cases
 "Nonideal" Cases
 Summary and Further Discussion
ShortTerm LP Analysis  Autocorrelation Method
 Covariance Method
 Solution Methods
 Gain Computation
 A Distance Measure for LP Coefficients
 Preemphasis of the Speech Waveform
Alternative Representations of the LP Coefficients  The Line Spectrum Pair
 Cepstral Parameters
Applications of LP in Speech Analysis  Pitch Estimation
 Formant Estimation and Glottal Waveform Deconvolution
Conclusions Problems Proof of Theorem 5.1 The Orthogonality Principle Cepstral Analysis Introduction "Real" Cepstrum  LongTerm Real Cepstrum
 ShortTerm Real Cepstrum
 Example Applications of the stRC to Speech Analysis and Recognition
 Other Forms and Variations on the stRC Parameters
Complex Cepstrum  LongTerm Complex Cepstrum
 ShortTerm Complex Cepstrum
 Example Application of the stCC to Speech Analysis
 Variations on the Complex Cepstrum
A Critical Analysis of the Cepstrum and Conclusions Problems Coding, Enhancement and Quality Assessment Speech Coding and Synthesis Introduction Optimum Scalar and Vector Quantization  Scalar Quantization
 Vector Quantization
Waveform Coding  Introduction
 Time Domain Waveform Coding
 Frequency Domain Waveform Coding
 Vector Waveform Quantization
Vocoders  The Channel Vocoder
 The Phase Vocoder
 The Cepstral (Homomorphic) Vocoder
 Formant Vocoders
 Linear Predictive Coding
 Vector Quantization of Model Parameters
Measuring the Quality of Speech Compression Techniques Conclusions Problems Quadrature Mirror Filters Speech Enhancement Introduction Classification of Speech Enhancement Methods ShortTerm Spectral Amplitude Techniques  Introduction
 Spectral Subtraction
 Summary of ShortTerm Spectral Magnitude Methods
Speech Modeling and Wiener Filtering  Introduction
 Iterative Wiener Filtering
 Speech Enhancement and AllPole Modeling
 Sequential Estimation via EM Theory
 Constrained Iterative Enhancement
 Further Refinements to Iterative Enhancement
 Summary of Speech Modeling and Wiener Filtering
Adaptive Noise Canceling  Introduction
 ANC Formalities and the LMS Algorithm
 Applications of ANC
 Summary of ANC Methods
Systems Based on Fundamental Frequency Tracking  Introduction
 SingleChannel ANC
 Adaptive Comb Filtering
 Harmonic Selection
 Summary of Systems Based on Fundamental Frequency Tracking
Performance Evaluation  Introduction
 Enhancement and Perceptual Aspects of Speech
 Speech Enhancement Algorithm Performance
Conclusions Problems The INTEL System Addressing CrossTalk in DualChannel ANC Speech Quality Assessment Introduction  The Need for Quality Assessment
 Quality Versus Intelligibility
Subjective Quality Measures  Intelligibility Tests
 Quality Tests
Objective Quality Measures  Articulation Index
 SignaltoNoise Ratio
 Itakura Measure
 Other Measures Based on LP Analysis
 WeightedSpectral Slope Measures
 Global Objective Measures
 Example Applications
Objective Versus Subjective Measures Problems Recognition The Speech Recognition Problem Introduction  The Dream and the Reality
 Discovering Our Ignorance
 Circumventing Our Ignorance
The "Dimensions of Difficulty"  SpeakerDependent Versus SpeakerIndependent Recognition
 Vocabulary Size
 IsolatedWord Versus ContinuousSpeech Recognition
 Linguistic Constraints
 Acoustic Ambiguity and Confusability
 Environmental Noise
Related Problems and Approaches  Knowledge Engineering
 Speaker Recognition and Verification
Conclusions Problems Dynamic Time Warping Introduction Dynamic Programming Dynamic Time Warping Applied to IWR  DTW Problem and Its Solution Using DP
 DTW Search Constraints
 Typical DTW Algorithm: Memory and Computational Requirements
DTW Applied to CSR  Introduction
 Level Building
 The OneStage Algorithm
 A GrammarDriven ConnectedWord Recognition System
 Pruning and Beam Search
 Summary of Resource Requirements for DTW Algorithms
Training Issues in DTW Algorithms Conclusions Problems The Hidden Markov Model Introduction Theoretical Developments  Generalities
 The Discrete Observation HMM
 The Continuous Observation HMM
 Inclusion of State Duration Probabilities in the Discrete Observation HMM
 Scaling the ForwardBackward Algorithm
 Training with Multiple Observation Sequences
 Alternative Optimization Criteria in the Training of HMMs
 A Distance Measure for HMMs
Practical Issues  Acoustic Observations
 Model Structure and Size
 Training with Insufficient Data
 Acoustic Units Modeled by HMMs
First View of Recognition Systems Based on HMMs  Introduction
 IWR Without Syntax
 CSR by the ConnectedWord Strategy Without Syntax
 Preliminary Comments on Language Modeling Using HMMs
Problems Language Modeling Introduction Formal Tools for Linguistic Processing  Formal Languages
 Perplexity of a Language
 BottomUp Versus TopDown Parsing
HMMs, FiniteState Automata, and Regular Grammars A "BottomUp" Parsing Example Principles of "TopDown" Recognizers  Focus on the Linguistic Decoder
 Focus on the Acoustic Decoder
 Adding Levels to the Linguistic Decoder
 Training the ContinuousSpeech Recognizer
Other Language Models  NGram Statistical Models
 Other Formal Grammars
IWR As "CSR" Standard Databases for SpeechRecognition Research A Survey of LanguageModelBased Systems Conclusions Problems The Artificial Neural Network Introduction The Artificial Neuron Network Principles and Paradigms  Introduction
 Layered Networks: Formalities and Definitions
 The Multilayer Perceptron
 Learning Vector Quantizer
Applications of ANNs in Speech Recognition  Presegmented Speech Material
 Recognizing Dynamic Speech
 ANNs and Conventional Approaches
 Language Modeling Using ANNs
 Integration of ANNs into the Survey Systems of Section 13.9
Conclusions Problems Index Hardcover; 908 pages
