close
1.

図書
図書
1. Perceiving talking faces : from speech perception to a behavioral principle (: hardcover)
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
所蔵情報: loading…
2.

図書
図書
2. Talker variability in speech processing
edited by Keith Johnson, John W. Mullennix
出版情報: San Diego ; Tokyo : Academic Press, c1997  xii, 237 p. ; 24 cm
所蔵情報: loading…
3.

図書
図書
3. Speech, language, and communication
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
所蔵情報: loading…
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
4.

図書
図書
4. Human communication : a unified view
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
所蔵情報: loading…
5.

図書
図書
5. Handbook of neural networks for speech processing
Shigeru Katagiri, editor
出版情報: Boston : Artech House, c2000  xxiii, 522 p. ; 24 cm
所蔵情報: loading…
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：
6.

図書
図書
6. Introduction to phonetics
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
所蔵情報: loading…
文献の複写および貸借の依頼を行う
 文献複写・貸借依頼