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1.

図書

図書
Dominic W. Massaro
出版情報: Cambridge, Mass. : MIT Press, c1998  xii, 494 p. ; 27 cm.
シリーズ名: MIT Press/Bradford Books series in cognitive psychology
Bradford book
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2.

図書

図書
by L.F. Brosnahan and Bertil Malmberg
出版情報: Cambridge : Heffer, 1970  ix, 243 p., [2] leaves of plates (1 folded) ; 23 cm
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3.

図書

図書
Shigeru Katagiri, editor
出版情報: Boston : Artech House, c2000  xxiii, 522 p. ; 24 cm
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目次情報: 続きを見る
Preface
Fundamentals / Part I:
Introduction / 1:
Speech Processing / 1.1:
Neural Networks / 1.2:
Taxonomy of Neural Networks / 1.2.1:
Overview / 1.2.2.1:
Structure / 1.2.2.2:
Measurement / 1.2.2.3:
Objective Function / 1.2.2.4:
Optimization / 1.2.2.5:
Neural Networks for Speech Processing / 1.3:
Handbook Overview / 1.4:
Part I: Fundamentals / 1.4.1:
Part II: Current Issues in Speech Recognition / 1.4.2:
Part III: Current Issues in Speech Signal Processing / 1.4.3:
References
The Speech Signal and Its Production Model / 2:
Information Conveyed by Speech / 2.1:
Linguistic Information / 2.2.1:
Segmental Features / 2.2.1.1:
Suprasegmental Features / 2.2.1.2:
Paralinguistic Information / 2.2.2:
Nonlinguistic Information / 2.2.3:
Idiosyncratic Factors / 2.2.3.1:
Emotional Factors / 2.2.3.2:
Hierarchical Speech Production Processes / 2.2.4:
Summary / 2.2.5:
Physical and Physiological Processes in Speech Production / 2.3:
Respiration System / 2.3.1:
Normal Breathing Without Speech Production / 2.3.1.1:
Expiration in Speech Production / 2.3.1.2:
Phonatory System / 2.3.2:
Framework of the Larynx / 2.3.2.1:
Abduction versus Adduction / 2.3.2.2:
F0 Control During Speech / 2.3.2.3:
Articulatory System / 2.3.3:
Morphology of Articulators / 2.3.3.1:
Vowel Production / 2.3.3.2:
Consonant Production / 2.3.3.3:
Models and Theories of Speech Production / 2.3.4:
Laryngeal System / 2.4.1:
Vocal Fold Vibration / 2.4.1.1:
F0 Control in Running Speech Production / 2.4.1.2:
Vertical Movements of the Larynx / 2.4.1.3:
Dynamic Characteristic of Articulators / 2.4.2:
Models of Individual Articulators / 2.4.2.1:
Articulatory Models of Speech Production / 2.4.2.2:
Conclusion / 2.4.3:
Acknowledgment
Speech Recognition / 3:
Hearing and Machine Recognition / 3.1:
Recognition-Oriented Speech Feature Representation / 3.2.2:
Sound Spectrogram: Time-Frequency-Energy Representation / 3.2.2.1:
Acoustic Feature Vector / 3.2.2.2:
Static Versus Dynamic Nature / 3.2.2.3:
Variety of Recognition Tasks / 3.2.3:
Recognition Mechanism / 3.2.4:
Example Task Setting / 3.2.4.1:
Distance-Based Recognition / 3.2.4.2:
Distance Computation Based on Dynamic Time Warping / 3.2.4.3:
Remarks / 3.2.4.4:
Bayes Decision Theory / 3.3:
Maximum Likelihood Estimation Approach / 3.3.1:
Bayesian Approach / 3.3.3:
Discriminant Function Approach / 3.3.4:
Example Task and Decision Rule / 3.3.4.1:
Loss / 3.3.4.2:
Design of Linear Discriminant Function Classifier / 3.3.4.3:
Acoustic Feature Extraction / 3.3.4.5:
Filter-Bank / 3.4.1:
Artificial Cochlea Filter / 3.4.1.1:
Fourier-Transform-Based Filter / 3.4.1.2:
Autoregressive Modeling / 3.4.2:
Cepstrum Modeling / 3.4.3:
Dynamic Feature Modeling / 3.4.4:
Probabilistic Acoustic Modeling Based on Hidden Markov Model / 3.5:
Principles of Hidden Markov Model / 3.5.1:
Selection of Output Probability Function / 3.5.2:
Discrete Model / 3.5.2.1:
Continuous Model / 3.5.2.2:
MLE-Based Design Method / 3.5.3:
Forward-Backward Method / 3.5.3.1:
Trellis Algorithm and Viterbi Algorithm / 3.5.4:
Discriminative Design Methods / 3.5.5:
Language Modeling / 3.6:
Role of Language Modeling / 3.6.1:
N-Gram Language Modeling / 3.6.2:
Concluding Remarks / 3.7:
Selection of Model Units / 3.7.1:
Open-Vocabulary Recognition / 3.7.3:
Bibliographical Remarks / 3.7.4:
Speech Coding / 4:
Attributes of Speech Coders / 4.1:
Basic Principles of Speech Coders / 4.3:
Quantization / 4.4:
Scalar Quantization / 4.4.1:
Vector Quantization / 4.4.2:
Linear Prediction / 4.5:
Linear Prediction Principles / 4.5.1:
Speech Coding Based on Linear Prediction / 4.5.2:
The Analysis-by-Synthesis Principle / 4.5.3:
Perceptual Filtering / 4.5.4:
Quantization of the Linear Prediction Coefficients / 4.5.5:
Sinusoidal Coding / 4.6:
Waveform Interpolation Methods / 4.7:
Subband Coding / 4.8:
Variable-Rate Coding / 4.9:
Basics / 4.9.1:
Phonetic Segmentation / 4.9.2:
Variable Rate Coders for ATM Networks / 4.9.3:
Voice over IP / 4.9.4:
Wideband Coders / 4.10:
Measuring Speech Coder Performance / 4.11:
Speech Coding over Noisy Channels / 4.12:
Speech Coding Standards / 4.13:
Conclusions / 4.14:
Current Issues in Speech Recognition / Part II:
Discriminative Prototype-Based Methods for Speech Recognition / 5:
The Bayes Decision Rule / 5.1:
Discriminant Functions / 5.2.2:
Discriminant Functions for Prototype-Based Methods / 5.2.3:
Example-Based Methods / 5.3:
Density Estimation / 5.3.1:
Estimation of Posterior Probabilities / 5.3.2:
The k-Nearest-Neighbor Method / 5.3.3:
The Nearest-Neighbor Rule / 5.3.3.1:
Error Bounds for k-Nearest-Neighbor Classification / 5.3.3.2:
Parzen Windows / 5.3.4:
An Example / 5.3.4.1:
Advantages and Limitations of Example-Based Methods / 5.3.5:
Smoothing / 5.3.6:
Applications to Speech Recognition / 5.3.7:
Prototype-Based Methods for Speech Recognition / 5.4:
Prototype-Based Classifier Design Using Minimum Classification Error / 5.5:
Definition of Discriminant Function / 5.5.1:
Definition of Misclassification Measure / 5.5.2:
Definition of Local Loss Function / 5.5.3:
Overall Loss Function and Optimization / 5.5.4:
Modified Newton's Method: The Quickprop Algorithm / 5.5.5:
Relation of MCE Loss to the Bayes Error / 5.5.6:
Choice of Smoothing Parameters for MCE-Based Optimization / 5.5.7:
Learning Vector Quantization / 5.6:
Shift-Tolerant LVQ for Speech Recognition / 5.6.1:
HMM Interpretation of STLVQ / 5.6.1.1:
Limitations and Strengths of STLVQ Architecture and Training / 5.6.1.2:
Expanding the Scope of LVQ for Speech Recognition: Incorporation into Hidden Markov Modeling / 5.6.2:
LVQ-HMM / 5.6.2.1:
HMM-LVQ / 5.6.2.2:
Minimum Classification Error Interpretation of LVQ / 5.6.3:
Smoothness of MCE Loss / 5.6.4:
LVQ Summary / 5.6.5:
Prototype-Based Methods Using Dynamic Programming / 5.7:
MCE-Trained Prototypes for DTW-Based Speech Recognition / 5.7.1:
Practical Implementation of MCE/GPD / 5.7.1.1:
MCE-DTW Results / 5.7.1.2:
Prototype-Based Minimum Error Classifier / 5.7.2:
PBMEC State Distance and Discriminant Function / 5.7.2.1:
MCE/GPD in the Context of Speech Recognition Using Phoneme Models / 5.7.2.2:
PBMEC Results / 5.7.2.3:
Summary of Prototype-Based Methods Using DP / 5.7.3:
Hidden Markov Model Design Based on MCE / 5.8:
HMM State Likelihood and Discriminant Function / 5.8.1:
MCE Misclassification Measure and Loss / 5.8.2:
Calculation of MCE Gradient for HMMs / 5.8.3:
Derivative of Loss with Respect to Misclassification Measure / 5.8.3.1:
Derivative of Misclassification Measure with Respect to Discriminant Functions / 5.8.3.2:
Derivative of Discriminant Function with Respect to Observation Probability Density Function / 5.8.3.3:
Derivative of Observation Probability with Respect to Mixing Weights / 5.8.3.4:
Derivative of Observation Probability with Respect to Mean Vectors / 5.8.3.5:
Derivative of Observation Probability with Respect to Covariances / 5.8.3.6:
Application of the Chain Rule / 5.8.3.7:
MCE-HMM Results / 5.8.4:
Recurrent Neural Networks for Speech Recognition / 5.9:
Background and Motivation / 6.1:
Chapter Overview / 6.1.2:
Speech Recognition Theory / 6.2:
Basics of Neural Networks / 6.3:
Parameter Estimation by Maximum Likelihood / 6.3.1:
Problem Classification / 6.3.2:
Regression / 6.3.2.1:
Classification / 6.3.2.2:
Neural Network Training / 6.3.3:
Gradient Descent Training / 6.3.3.1:
RPROP Training / 6.3.3.2:
ARPROP Training / 6.3.3.3:
Neural Network Architectures / 6.3.4:
Multilayer Perceptrons / 6.3.4.1:
Time-Delay Neural Networks / 6.3.4.2:
Recurrent Neural Networks / 6.4:
Unidirectional Recurrent Neural Network / 6.4.1:
RNN Architecture / 6.4.1.1:
RNN Training / 6.4.1.2:
Bidirectional Recurrent Neural Network / 6.4.2:
BRNN Architecture / 6.4.2.1:
BRNN Training / 6.4.2.2:
Modeling Phonetic Context / 6.5:
System Training and Usage / 6.6:
Training / 6.6.1:
Usage / 6.6.2:
Discussion / 6.7:
Training Criterion / 6.7.1.1:
Discriminative Training / 6.7.1.2:
Distribution of Model Complexity / 6.7.1.3:
Time-Delay Neural Networks and NN/HMM Hybrids: A Family of Connectionist Continuous-Speech Recognition Systems / 6.7.2:
MS-TDNNs and NN/HMM Hybrid Approaches / 7.1:
The Time-Delay Neural Network (TDNN) / 7.2.1:
Multistate TDNN / 7.2.2:
MS-TDNN Variants / 7.2.3:
Hybrid NN/HMM Variants / 7.2.4:
Alphabet Recognition with the MS-TDNN / 7.3:
Training Procedures / 7.3.1:
Duration Modeling / 7.3.2:
Experiments / 7.3.3:
Speaker-Dependent Data / 7.3.3.1:
Speaker-Independent Data / 7.3.3.2:
Telephone Data / 7.3.3.3:
Searching in Large Name Lists / 7.3.4:
Multimodal Input: Lipreading / 7.4:
Motivation / 7.4.1:
The Recognizer / 7.4.2:
Results / 7.4.3:
Modular Neural Networks / 7.5:
Architecture / 7.5.1:
Application to NN/HMM Models / 7.5.2:
Experiments with a Hybrid HME/HMM System / 7.5.3:
Context Modeling / 7.6:
Clustering Context Classes / 7.6.1:
Factoring Context-Dependent Posteriors / 7.6.2:
Hierarchies of Neural Networks / 7.6.3:
Manually Structured Hierarchies / 7.6.3.1:
Clustering Hierarchies of Neural Networks / 7.6.3.2:
Experiments and Results / 7.6.4:
Probability-Oriented Neural Networks and Hybrid Connectionist/Stochastic Networks / 8:
Fundamentals of Probability-Oriented Neural Networks / 8.1:
The Bayes Decision Framework / 8.2.1:
Types of PONNs / 8.2.2:
Radial Basis Function Networks / 8.2.3.1:
Probabilistic Neural Networks / 8.2.3.2:
Learning Methods for PNNs / 8.2.4:
Position of the Problem / 8.3.1:
MLE and EM Algorithms for PNNs / 8.3.2:
MMIE for PNNs / 8.3.3:
Applications to Automatic Speech Recognition / 8.4:
Speaker Recognition / 8.4.1:
Hybrid Connectionist/Stochastic Models / 8.5:
Proposed Solutions / 8.5.1:
ANNs as Front-Ends for HMMs / 8.5.2.1:
ANNs as Postprocessors of HMMs / 8.5.2.2:
Unified Models / 8.5.2.3:
Minimum Classification Error Networks / 8.5.3:
Speech Pattern Recognition Using Modular Systems / 9.1:
Classifier Design / 9.1.2:
What Is an Artificial Neural Network? / 9.1.3:
Minimum Recognition Error Network / 9.1.4:
Chapter Organization / 9.1.5:
Discriminative Pattern Classification / 9.2:
Minimum Error Rate Classification / 9.2.1:
Generalized Probabilistic Descent Method / 9.2.3:
Formalization Fundamentals / 9.3.1:
Distance Classifier for Classifying Dynamic Patterns: Preparation / 9.3.2.1:
Emulation of Decision Process / 9.3.2.2:
Selection of Loss Functions / 9.3.2.3:
Design Optimality in Practical Situations / 9.3.2.4:
GPD-Based Classifier Design / 9.3.3:
E-Set Task / 9.3.3.1:
P-Set Task / 9.3.3.2:
Derivatives of GPD / 9.4:
Segmental GPD for Continuous Speech Recognition / 9.4.1:
Minimum Error Training for Open-Vocabulary Speech Recognition / 9.4.3:
Open-Vocabulary Speech Recognition / 9.4.3.1:
Minimum Spotting Error Learning / 9.4.3.2:
Discriminative Utterance Verification / 9.4.3.3:
Discriminative Feature Extraction / 9.4.4:
An Example Implementation for Cepstrum-Based Speech Recognition / 9.4.4.1:
Discriminative Metric Design / 9.4.4.3:
Minimum Error Learning Subspace Method / 9.4.4.4:
Speaker Recognition Using GPD / 9.4.5:
Acknowledgments / 9.5:
Probabilistic Descent Theorem for Probability-Based Discriminant Functions / Appendix 1:
Relationships Between MCE/GPD and Others / Appendix 2:
Current Issues in Speech Signal Processing / Part III:
Networks for Speaker Recognition / 10:
Speaker Recognition Overview / 10.1:
Discriminative Information / 10.3:
Supervised Training / 10.3.1:
Cohort Normalization / 10.3.2:
Speaker Recognition Networks / 10.4:
Multilayer Perceptron / 10.4.1:
Radial Basis Functions / 10.4.2:
Decision Trees / 10.4.3:
Neural Tree Network / 10.4.7:
Performance Summary / 10.4.8:
Model Combination / 10.5:
Model Combination Approaches / 10.5.1:
Linear Opinion Pool / 10.5.1.1:
Log Opinion Pool / 10.5.1.2:
Voting Methods / 10.5.1.3:
Error Correlation Analysis / 10.5.2:
Two-Model Combination / 10.5.3:
Three-Model Combination / 10.5.4:
Neural Networks for Voice Conversion / 10.6:
Introduction: Speech and Speaker Characteristics / 11.1:
Studies in Voice Conversion / 11.2:
Neural Networks for Transformation of Vocal Tract Shapes / 11.3:
Linear Approximation of Formant Transformation / 11.3.1:
Neural Network Models / 11.3.2:
Generalization / 11.3.3:
Implementation of Voice Conversion / 11.4:
Voice Transformation System / 11.4.1:
Normalization of Intonational Features / 11.4.2:
Evaluation of Voice Transformation / 11.4.3:
Neural Networks for Speech Coding / 11.5:
Source Coding and Neural Networks / 12.1:
Source Coding / 12.2.1:
Source Coding with Neural Networks / 12.2.2:
Vector Quantization with Kohonen Self-Organizing Feature Maps / 12.2.3.1:
Multilayer Neural Network as Front-End of a Coder / 12.2.3.2:
Codebook-Excited Neural Networks / 12.2.3.3:
Quantization Performance of Neural Networks / 12.3:
Kohonen Self-Organizing Feature Maps / 12.3.1:
Architecture and Training Process / 12.3.1.1:
Conditional Histogram Neural Network FSVQ / 12.3.1.2:
Nearest-Neighbor Neural Network FSVQ / 12.3.1.3:
Simulations / 12.3.1.4:
Coders with Neural Network Front-Ends / 12.3.2:
Speech Coding with Neural Networks / 12.3.3:
Coding Speech Spectrum with Neural Networks / 12.4.1:
Nonlinear Prediction Speech Coding / 12.4.2:
A Neural Model of Nonlinear Prediction / 12.4.2.1:
Nonlinear Predictive Vector Quantization / 12.4.2.2:
Nonlinear Predictive Quantization Performance / 12.4.2.3:
Code-Excited Nonlinear Predictive Speech Coding / 12.4.3:
Nonlinear Predictive Filter Tolerance for an Excitation Disturbance / 12.4.3.1:
Gain-Adaptive Nonlinear Predictive Coding / 12.4.3.2:
Coding Performance / 12.4.3.3:
Networks for Speech Enhancement / 12.4.4:
Background / 13.1:
Model Structure / 13.1.2:
Neural Time-Domain Filtering Methods / 13.1.3:
Direct Time-Domain Mapping / 13.2.1:
Extended Kalman Filtering with Predictive Models / 13.2.2:
Neural Transform-Domain Methods / 13.3:
Spectral Subtraction / 13.3.1:
Neural Transform-Domain Mappings / 13.3.2:
State-Dependent Model Switching Methods / 13.4:
Classification Switched Models / 13.4.1:
Hybrid HMM and EKF / 13.4.2:
Online Iterative Methods / 13.5:
Online Predictive Enhancement / 13.5.1:
Maximum-Likelihood Estimation and Dual Kalman Filtering / 13.5.2:
Noise-Regularized Adaptive Filtering / 13.5.3:
Summary and Conclusions / 13.6:
Index
Preface
Fundamentals / Part I:
Introduction / 1:
4.

図書

図書
edited by Keith Johnson, John W. Mullennix
出版情報: San Diego ; Tokyo : Academic Press, c1997  xii, 237 p. ; 24 cm
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5.

図書

図書
edited by Joanne L. Miller, Peter D. Eimas
出版情報: San Diego ; Tokyo : Academic Press, c1995  xviii, 415 p. ; 24 cm
シリーズ名: Handbook of perception and cognition / editors, Edward C. Carterette, Morton P. Friedman
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目次情報: 続きを見る
Issues of Representation in Psycholinguistics / L. Frazier
Speech Production / C.A. Fowler
Speech Perception: New Directions in Research and Theory / L.C. Nygaard ; D.B. Pisoni
Spoken Word Recognition and Production / A. Cutler
Visual Word Recognition: An Overview / M.S. Seidenberg
Sentence Production: From Mind to Mouth / K. Bock
Sentence Comprehension / M.K. Tanenhaus ; J.C. Trueswell
Language Acquisition: Speech Sounds and the Beginnings of Phonology / P.W. Jusczyk
Language Acquisition: The Lexicon and Syntax / E.V. Clark
The Neurobiology of Language / S.E. Blumstein
Pragmatics and Discourse / H.H. Clark ; B. Bly
Subject Index
Issues of Representation in Psycholinguistics / L. Frazier
Speech Production / C.A. Fowler
Speech Perception: New Directions in Research and Theory / L.C. Nygaard ; D.B. Pisoni
6.

図書

図書
Edited by Edward E. David, Jr. [and] Peter B. Denes
出版情報: New York : McGraw-Hill, 1972  xvii, 458 p ; 24 cm
シリーズ名: Inter-university electronics series ; no. 15
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