| Series Introduction |
|
v | |
| Preface |
|
vii | |
|
I ANALYTICAL BACKGROUND AND TECHNIQUES |
|
|
1 | (200) |
|
Discrete-Time Signals, Systems, and Transforms |
|
|
3 | (26) |
|
|
|
3 | (8) |
|
|
|
4 | (1) |
|
|
|
4 | (2) |
|
Practical cases of sampling |
|
|
6 | (5) |
|
Discrete-Time Systems and z-Transforms |
|
|
11 | (5) |
|
Classification of systems |
|
|
11 | (3) |
|
Fundamentals of linear time-invariant systems |
|
|
14 | (1) |
|
|
|
15 | (1) |
|
Characterizations of Digital Filters |
|
|
16 | (4) |
|
Filter transfer functions |
|
|
16 | (1) |
|
Filters described by difference equations |
|
|
17 | (1) |
|
Poles and zeros in a digital filter |
|
|
17 | (3) |
|
Frequency Responses of Digital Filters |
|
|
20 | (3) |
|
Frequency response as related to pole and zero locations |
|
|
20 | (1) |
|
|
|
21 | (1) |
|
|
|
22 | (1) |
|
Discrete Fourier Transform |
|
|
23 | (2) |
|
|
|
23 | (1) |
|
Frequency range and frequency resolution of the DFT |
|
|
24 | (1) |
|
|
|
25 | (1) |
|
Short-Time Fourier transform |
|
|
25 | (3) |
|
Definition of STFT: two alternative views |
|
|
26 | (1) |
|
STFT magnitude (spectrogram) |
|
|
27 | (1) |
|
|
|
28 | (1) |
|
Analysis of Discrete-Time Speech Signals |
|
|
29 | (36) |
|
Time-Frequency Analysis of Speech |
|
|
29 | (12) |
|
Time-domain and frequency-domain properties of speech |
|
|
30 | (6) |
|
Joint time-frequency properties of speech |
|
|
36 | (3) |
|
|
|
39 | (2) |
|
Analysis Based on Linear Predictive Coding |
|
|
41 | (9) |
|
Least-squares estimate of LPC coefficients |
|
|
42 | (1) |
|
Autocorrelation and covariance methods |
|
|
43 | (3) |
|
Spectral estimation via LPC |
|
|
46 | (2) |
|
|
|
48 | (1) |
|
Choice of order of the LPC model |
|
|
49 | (1) |
|
|
|
50 | (1) |
|
Cepstral Analysis of Speech |
|
|
50 | (3) |
|
|
|
50 | (2) |
|
Mel-frequency cepstral coefficients |
|
|
52 | (1) |
|
Automatic Extraction and Tracking of Speech Formants |
|
|
53 | (3) |
|
Formants and vocal tract resonances |
|
|
53 | (2) |
|
Formant extraction and tracking methods |
|
|
55 | (1) |
|
Automatic Extraction of Voicing Pitch |
|
|
56 | (5) |
|
Basics of pitch estimation methods |
|
|
57 | (1) |
|
Time-domain F0 estimation |
|
|
58 | (1) |
|
Short-time spectral techniques for F0 estimation |
|
|
59 | (2) |
|
Auditory Models for Speech Analysis |
|
|
61 | (2) |
|
Perceptual linear prediction |
|
|
61 | (1) |
|
|
|
62 | (1) |
|
|
|
63 | (2) |
|
Probability and Random Processes |
|
|
65 | (32) |
|
Random Variables, Distributions, and Summary Statistics |
|
|
65 | (10) |
|
Random variables and their distributions |
|
|
65 | (1) |
|
Summary statistics --- expectations, moments, and covariances |
|
|
66 | (2) |
|
|
|
68 | (5) |
|
|
|
73 | (2) |
|
Conditioning, Total Probability Theorem, and Bayes' Rule |
|
|
75 | (7) |
|
Conditional probability, conditional PDF, and conditional independence |
|
|
75 | (3) |
|
The total probability theorem |
|
|
78 | (1) |
|
Bayes' rule and its sequential form |
|
|
79 | (3) |
|
|
|
82 | (1) |
|
Discrete-Time Random Processes |
|
|
83 | (4) |
|
Summary statistics of a random sequence |
|
|
83 | (1) |
|
Stationary random sequences |
|
|
84 | (2) |
|
White sequence, Markov sequence, Gauss-Markov sequence, and Wiener sequence |
|
|
86 | (1) |
|
Markov Chain and Hidden Markov Sequence |
|
|
87 | (7) |
|
Markov chain as discrete-state Markov sequence |
|
|
87 | (1) |
|
From Markov chain to hidden Markov sequence |
|
|
88 | (6) |
|
|
|
94 | (3) |
|
Linear Model and Dynamic System Model |
|
|
97 | (24) |
|
|
|
97 | (3) |
|
Canonical form of the model |
|
|
98 | (1) |
|
Examples of the linear model |
|
|
98 | (2) |
|
|
|
100 | (1) |
|
Time-Varying Linear Model |
|
|
100 | (9) |
|
Time-varying linear predictive model |
|
|
100 | (2) |
|
Markov modulated linear predictive model |
|
|
102 | (1) |
|
Markov modulated linear regression model |
|
|
102 | (2) |
|
Speech data and the time-varying linear models |
|
|
104 | (5) |
|
Linear Dynamic System Model |
|
|
109 | (6) |
|
State space formulation of the model |
|
|
112 | (1) |
|
Relationship to high-dimensional linear model |
|
|
113 | (1) |
|
|
|
114 | (1) |
|
Time-Varying Linear Dynamic System Model |
|
|
115 | (1) |
|
From time-invariant model to time-varying model |
|
|
115 | (1) |
|
|
|
116 | (1) |
|
Non-Linear Dynamic System Model |
|
|
116 | (4) |
|
From linear model to nonlinear model |
|
|
116 | (1) |
|
Nonlinearity and its approximations |
|
|
117 | (3) |
|
|
|
120 | (1) |
|
Optimization Methods and Estimation Theory |
|
|
121 | (58) |
|
Classical Optimization Techniques |
|
|
122 | (4) |
|
Basic definitions and results |
|
|
122 | (2) |
|
Necessary and sufficient conditions for an optimum |
|
|
124 | (1) |
|
Lagrange multiplier method for constrained optimization |
|
|
125 | (1) |
|
Numerical Methods for Optimization |
|
|
126 | (4) |
|
Methods based on finding roots of equations |
|
|
126 | (2) |
|
Methods based on gradient descent |
|
|
128 | (2) |
|
Dynamic Programming Techniques for Optimization |
|
|
130 | (5) |
|
|
|
131 | (1) |
|
Dynamic programming for the hidden Markov model |
|
|
132 | (2) |
|
Dynamic programming for the trended hidden Markov model |
|
|
134 | (1) |
|
Preliminaries of Estimation Theory |
|
|
135 | (8) |
|
Cramer-Rao lower bound and minimum variance unbiased estimator |
|
|
136 | (2) |
|
Example: MVU estimator for generalized linear model |
|
|
138 | (1) |
|
|
|
139 | (1) |
|
Best linear unbiased estimator |
|
|
140 | (2) |
|
|
|
142 | (1) |
|
|
|
143 | (6) |
|
|
|
143 | (1) |
|
Least squares estimator for the linear model |
|
|
144 | (2) |
|
Order-recursive least squares |
|
|
146 | (1) |
|
|
|
147 | (1) |
|
|
|
148 | (1) |
|
Maximum Likelihood Estimation |
|
|
149 | (11) |
|
Basic MLE procedure for fully observed data |
|
|
149 | (4) |
|
|
|
153 | (1) |
|
EM algorithm --- Introduction |
|
|
153 | (4) |
|
EM algorithm example --- Markov modulated Poisson process |
|
|
157 | (3) |
|
Estimation of Random Parameters |
|
|
160 | (8) |
|
Minimum mean square error (MMSE) estimator |
|
|
161 | (2) |
|
|
|
163 | (1) |
|
General Bayesian estimators and MAP estimator |
|
|
163 | (2) |
|
Linear minimum mean square error (LMMSE) estimator |
|
|
165 | (2) |
|
Sequential LMMSE estimator |
|
|
167 | (1) |
|
|
|
168 | (8) |
|
Generic Kalman filter algorithm |
|
|
169 | (2) |
|
Kalman filter algorithms for the linear state-space system |
|
|
171 | (2) |
|
Extended Kalman filter for nonlinear dynamic systems |
|
|
173 | (3) |
|
|
|
176 | (3) |
|
Statistical Pattern Recognition |
|
|
179 | (22) |
|
|
|
180 | (2) |
|
Bayes' risk and MAP decision rule |
|
|
180 | (1) |
|
|
|
181 | (1) |
|
Minimum Classification Error Criterion for Recognizer Design |
|
|
182 | (2) |
|
MCE classifier design steps |
|
|
182 | (1) |
|
Optimization of classifier parameters |
|
|
183 | (1) |
|
Hypothesis Testing and the Verification Problem |
|
|
184 | (4) |
|
MAP decision rule and hypothesis testing |
|
|
184 | (1) |
|
Verification problem in pattern recognition |
|
|
185 | (1) |
|
Neymann-Pearson approach to verification |
|
|
186 | (1) |
|
Bayesian approach to verification |
|
|
187 | (1) |
|
|
|
188 | (10) |
|
Discriminative training for HMM |
|
|
188 | (2) |
|
Discriminative training for the trended HMM |
|
|
190 | (3) |
|
Discriminative feature extraction |
|
|
193 | (3) |
|
Bayesian approach to verification using the Gaussian mixture model |
|
|
196 | (2) |
|
|
|
198 | (3) |
|
II FUNDAMENTALS OF SPEECH SCIENCE |
|
|
201 | (94) |
|
|
|
203 | (60) |
|
|
|
203 | (1) |
|
Articulatory Phonetics and Speech Generation |
|
|
203 | (15) |
|
Anatomy and physiology of the vocal tract |
|
|
204 | (3) |
|
Major features of speech articulation |
|
|
207 | (3) |
|
Phonemes, coarticulation, and acoustics |
|
|
210 | (3) |
|
Source-filter description of speech production |
|
|
213 | (5) |
|
Acoustic Models of Speech Production |
|
|
218 | (9) |
|
Resonances in a nonuniform vocal tract model |
|
|
218 | (2) |
|
|
|
220 | (1) |
|
Three-tube consonant modeling |
|
|
221 | (1) |
|
Speech production involving both poles and zeros |
|
|
222 | (2) |
|
Transmission line analog of the vocal tract |
|
|
224 | (3) |
|
Coarticulation: Its Origins and Models |
|
|
227 | (5) |
|
Effects of coarticulation |
|
|
228 | (1) |
|
Coarticulation effects for different articulators |
|
|
229 | (1) |
|
|
|
230 | (1) |
|
Effects of coarticulation on duration |
|
|
231 | (1) |
|
Models for coarticulation |
|
|
231 | (1) |
|
Acoustic-Phonetics and Characterization of Speech Signals |
|
|
232 | (6) |
|
|
|
233 | (2) |
|
Diphthongs and diphthongization |
|
|
235 | (1) |
|
|
|
235 | (1) |
|
|
|
235 | (1) |
|
|
|
236 | (1) |
|
|
|
237 | (1) |
|
Introduction to Auditory Phonetics |
|
|
238 | (2) |
|
|
|
239 | (1) |
|
|
|
239 | (1) |
|
|
|
239 | (1) |
|
|
|
240 | (6) |
|
|
|
241 | (1) |
|
Just-noticeable differences (JNDs) |
|
|
241 | (1) |
|
|
|
241 | (1) |
|
|
|
242 | (1) |
|
|
|
243 | (1) |
|
Nonsimultaneous (temporal) masking |
|
|
243 | (1) |
|
Just-noticeable differences (JNDs) in speech |
|
|
244 | (1) |
|
|
|
245 | (1) |
|
|
|
246 | (15) |
|
Physical aspects of speech important for perception |
|
|
246 | (1) |
|
Experiments using synthetic speech |
|
|
247 | (1) |
|
Models of speech perception |
|
|
248 | (3) |
|
|
|
251 | (2) |
|
|
|
253 | (3) |
|
Duration as a phonemic cue |
|
|
256 | (1) |
|
Perception of intonational features |
|
|
257 | (4) |
|
|
|
261 | (2) |
|
|
|
263 | (32) |
|
|
|
263 | (1) |
|
Phonemes: Minimal Contrastive Units of Speech Sounds |
|
|
264 | (2) |
|
|
|
264 | (1) |
|
|
|
265 | (1) |
|
Features: Basic Units of Phonological Representation |
|
|
266 | (4) |
|
Why a phonemic approach is not adequate |
|
|
266 | (1) |
|
|
|
267 | (3) |
|
|
|
270 | (1) |
|
Phonological Rules Expressed by Features |
|
|
270 | (4) |
|
Formalization of phonological rules |
|
|
271 | (1) |
|
Common phonological rule types |
|
|
271 | (3) |
|
Feature Geometry --- Internal Organization of Speech Sounds |
|
|
274 | (6) |
|
|
|
274 | (1) |
|
From linear phonology to nonlinear phonology |
|
|
274 | (2) |
|
|
|
276 | (1) |
|
Phonological rules in feature geometry |
|
|
277 | (3) |
|
|
|
280 | (8) |
|
Articulatory gestures and task dynamics |
|
|
281 | (4) |
|
|
|
285 | (2) |
|
Phonological contrast in articulatory phonology |
|
|
287 | (1) |
|
Syllables: External Organization of Speech Sounds |
|
|
288 | (5) |
|
The representation of syllable structure |
|
|
288 | (2) |
|
Phonological function of the syllable: basic phonotactic unit |
|
|
290 | (3) |
|
|
|
293 | (2) |
|
III COMPUTATIONAL PHONOLOGY AND PHONETICS |
|
|
295 | (136) |
|
|
|
297 | (36) |
|
Articulatory Features and a System for Their Specification |
|
|
298 | (4) |
|
Cross-Tier Overlapping of Articulatory Features |
|
|
302 | (10) |
|
Major and secondary articulatory features |
|
|
302 | (2) |
|
Feature assimilation and overlapping examples |
|
|
304 | (3) |
|
Constraining rules for feature overlapping |
|
|
307 | (5) |
|
Constructing Discrete Articulatory States |
|
|
312 | (4) |
|
Motivations from symbolic pronunciation modeling in speech recognition |
|
|
312 | (2) |
|
Articulatory state construction |
|
|
314 | (2) |
|
Use of High-Level Linguistic Constraints |
|
|
316 | (3) |
|
Types of high-level linguistic constraints |
|
|
316 | (1) |
|
A parser for English syllable structure |
|
|
317 | (2) |
|
Implementation of Feature Overlapping Using Linguistic Constraints |
|
|
319 | (12) |
|
|
|
320 | (1) |
|
A generator of overlapping feature bundles: Overview and examples of its output |
|
|
321 | (3) |
|
Demi-syllable as the rule organizational unit |
|
|
324 | (2) |
|
Phonological rule formulation |
|
|
326 | (5) |
|
|
|
331 | (2) |
|
Computational Models for Speech Production |
|
|
333 | (50) |
|
Introduction and Overview of Speech Production Modeling |
|
|
334 | (3) |
|
Two types of speech production modeling and research |
|
|
334 | (2) |
|
Multiple levels of dynamics in human speech production |
|
|
336 | (1) |
|
Modeling Acoustic Dynamics of Speech |
|
|
337 | (17) |
|
Hidden Markov model viewed as a generative model for acoustic dynamics |
|
|
337 | (3) |
|
From stationary state to nonstationary state |
|
|
340 | (1) |
|
ML learning for the trended HMM via the EM algorithm |
|
|
341 | (4) |
|
Example: Model with state-dependent polynomial trends |
|
|
345 | (1) |
|
Recursively-defined acoustic trajectory model using a linear dynamic system |
|
|
346 | (2) |
|
ML learning for linear dynamic system |
|
|
348 | (6) |
|
Modeling Hidden Dynamics of Speech |
|
|
354 | (9) |
|
Derivation of discrete-time hidden-dynamic state equation |
|
|
355 | (2) |
|
Nonlinear state space formulation of hidden dynamic model |
|
|
357 | (1) |
|
Task dynamics, articulatory dynamics, and vocal-tract resonance dynamics |
|
|
357 | (6) |
|
Hidden Dynamic Model Implemented Using Piecewise Linear Approximation |
|
|
363 | (11) |
|
Motivations and a new form of the model formulation |
|
|
365 | (1) |
|
Parameter estimation algorithm |
|
|
366 | (8) |
|
Likelihood-scoring algorithm |
|
|
374 | (1) |
|
A Comprehensive Statistical Generative Model of the Dynamics of Casual Speech |
|
|
374 | (7) |
|
Overlapping model for multi-tiered phonological construct |
|
|
376 | (1) |
|
|
|
377 | (1) |
|
Functional model for hidden articulatory dynamics |
|
|
378 | (1) |
|
Functional model for articulatory-to-acoustic mapping |
|
|
379 | (2) |
|
|
|
381 | (2) |
|
Computational Models for Auditory Speech Processing |
|
|
383 | (48) |
|
A Computational Model for the Cochlear Function |
|
|
384 | (4) |
|
|
|
384 | (2) |
|
Mathematical formulation of the cochlear model |
|
|
386 | (2) |
|
Frequency-Domain Solution of the Cochlear Model |
|
|
388 | (2) |
|
Time-Domain Solution of the Cochlear Model |
|
|
390 | (2) |
|
Stability Analysis for Time-Domain Solution of the Cochlear Model |
|
|
392 | (7) |
|
Derivation of the stability condition |
|
|
392 | (6) |
|
Application of the stability analysis |
|
|
398 | (1) |
|
Computational Models for Inner Hair Cells and for Synapses to Auditory Nerve Fibers |
|
|
399 | (2) |
|
The inner hair cell model |
|
|
399 | (1) |
|
|
|
399 | (2) |
|
Interval-Based Speech Feature Extraction from the Cochlear Model Outputs |
|
|
401 | (4) |
|
Inter-peak interval histogram construction |
|
|
401 | (1) |
|
Matching neural and modeled IPIHs for tuning BM-model's parameters |
|
|
402 | (3) |
|
Interval-Histogram Representation for the Speech Sound in Quiet and in Noise |
|
|
405 | (5) |
|
Inter-peak interval histograms for clean speech |
|
|
406 | (2) |
|
Inter-peak interval histograms for noisy speech |
|
|
408 | (2) |
|
Computational Models for Network Structures in the Auditory Pathway |
|
|
410 | (18) |
|
|
|
411 | (2) |
|
Modeling action potential generation in the auditory nerve |
|
|
413 | (2) |
|
Neural-network models central to the auditory nerve |
|
|
415 | (8) |
|
Model simulation with speech inputs |
|
|
423 | (2) |
|
|
|
425 | (3) |
|
|
|
428 | (3) |
|
IV SPEECH TECHNOLOGY IN SELECTED AREAS |
|
|
431 | (150) |
|
|
|
433 | (78) |
|
|
|
433 | (4) |
|
The speech recognition problem |
|
|
434 | (1) |
|
ASR system specifications |
|
|
435 | (1) |
|
|
|
436 | (1) |
|
Evaluation measures for speech recognizers |
|
|
437 | (1) |
|
Mathematical Formulation of Speech Recognition |
|
|
437 | (3) |
|
|
|
437 | (1) |
|
Acoustic model, language model, and sequential optimization |
|
|
438 | (1) |
|
Differentially weighting acoustic and language models |
|
|
439 | (1) |
|
Word insertion penalty factor |
|
|
439 | (1) |
|
|
|
440 | (3) |
|
What is acoustic pre-processing |
|
|
440 | (1) |
|
Some common acoustic pre-processors |
|
|
441 | (2) |
|
Use of HMMs in Acoustic Modeling |
|
|
443 | (3) |
|
|
|
443 | (1) |
|
Relationships between HMM states and speech units |
|
|
444 | (1) |
|
Construction of context-dependent HMMs |
|
|
444 | (1) |
|
Some advantages of the HMM formulation for ASR |
|
|
445 | (1) |
|
Use of Higher-Order Statistical Models in Acoustic Modeling |
|
|
446 | (5) |
|
Why higher-order models are needed |
|
|
446 | (1) |
|
Stochastic segment models for speech acoustics |
|
|
447 | (2) |
|
Super-segmental, hidden dynamic models |
|
|
449 | (1) |
|
Higher-order pronunciation models |
|
|
450 | (1) |
|
Case Study I: Speech Recognition Using a Hidden Dynamic Model |
|
|
451 | (12) |
|
|
|
452 | (1) |
|
|
|
453 | (2) |
|
Learning model parameters |
|
|
455 | (4) |
|
Likelihood-scoring algorithm |
|
|
459 | (1) |
|
Experiments on spontaneous speech recognition |
|
|
459 | (4) |
|
Case Study II: Speech Recognition Using HMMs Structured by Locus Equations |
|
|
463 | (15) |
|
|
|
463 | (1) |
|
|
|
464 | (2) |
|
Learning locus-HMM parameters |
|
|
466 | (8) |
|
Phonetic classification experiments |
|
|
474 | (4) |
|
Robustness of Acoustic Modeling and Recognizer Design |
|
|
478 | (4) |
|
|
|
478 | (1) |
|
Model-space robustness by adaptation |
|
|
479 | (2) |
|
|
|
481 | (1) |
|
Case Study III: MAP Approach to Speaker Adaptation Using Trended HMMs |
|
|
482 | (9) |
|
Derivation of MAP estimates for the trended HMM |
|
|
483 | (4) |
|
Speaker adaptation experiments |
|
|
487 | (4) |
|
Case Study IV: Bayesian Adaptive Training for Compensating Acoustic Variability |
|
|
491 | (13) |
|
|
|
492 | (2) |
|
Overview of the compensation strategy |
|
|
494 | (1) |
|
Bayesian adaptive training algorithm |
|
|
495 | (2) |
|
Robust decoding using Bayesian predictive classification |
|
|
497 | (3) |
|
Experiments on spontaneous speech recognition |
|
|
500 | (4) |
|
Statistical Language Modeling |
|
|
504 | (5) |
|
|
|
504 | (1) |
|
|
|
504 | (2) |
|
Decision-tree language modeling |
|
|
506 | (1) |
|
Context-free grammar as a language model |
|
|
506 | (1) |
|
Maximum-entropy language modeling |
|
|
507 | (1) |
|
Adaptive language modeling |
|
|
508 | (1) |
|
|
|
509 | (2) |
|
|
|
511 | (48) |
|
|
|
511 | (1) |
|
Classification of Basic Techniques for Speech Enhancement |
|
|
512 | (2) |
|
Classification by what and how information is used |
|
|
512 | (1) |
|
Classification by waveform or feature as the output |
|
|
512 | (1) |
|
Classification by single or multiple sensors |
|
|
513 | (1) |
|
Classification by the general approaches employed |
|
|
513 | (1) |
|
|
|
514 | (2) |
|
|
|
516 | (1) |
|
Use of HMM as the Prior Model for Speech Enhancement |
|
|
517 | (6) |
|
Training AR-HMMs for clean speech and for noise |
|
|
518 | (1) |
|
The MAP enhancement technique |
|
|
518 | (1) |
|
The approximate MAP enhancement technique |
|
|
519 | (1) |
|
The MMSE enhancement technique |
|
|
520 | (2) |
|
|
|
522 | (1) |
|
Case Study I: Implementation and Evaluation of HMM-Based MMSE Enhancement |
|
|
523 | (6) |
|
Double pruning the MMSE filter weights |
|
|
524 | (1) |
|
PDF approximation for noisy speech |
|
|
524 | (2) |
|
Overview of speech enhancement system and experiments |
|
|
526 | (1) |
|
Enhancement results using SNR as an evaluation measure |
|
|
527 | (2) |
|
Enhancement results using subjective evaluation |
|
|
529 | (1) |
|
Case Study II: Use of the Trended HMM for Speech Enhancement |
|
|
529 | (15) |
|
Formulation of the prior model |
|
|
530 | (1) |
|
Derivation of the MMSE estimator using the prior model |
|
|
531 | (5) |
|
Implementation of the MMSE enhancement technique |
|
|
536 | (2) |
|
Approximate MMSE enhancement technique |
|
|
538 | (1) |
|
|
|
539 | (4) |
|
Speech waveform enhancement results |
|
|
543 | (1) |
|
Use of Speech Feature Enhancement for Robust Speech Recognition |
|
|
544 | (13) |
|
Roles of speech enhancement in feature-space robust ASR |
|
|
544 | (1) |
|
A statistical model for log-domain acoustic distortion |
|
|
545 | (3) |
|
Use of prior models for clean speech and for noise |
|
|
548 | (2) |
|
Use of the MMSE estimator |
|
|
550 | (1) |
|
MMSE estimator with prior speech model of static features |
|
|
551 | (2) |
|
Estimation with prior speech model for joint static and dynamic features |
|
|
553 | (2) |
|
|
|
555 | (2) |
|
|
|
557 | (2) |
|
|
|
559 | (22) |
|
|
|
559 | (2) |
|
|
|
561 | (1) |
|
|
|
562 | (1) |
|
|
|
563 | (8) |
|
Articulatory method for speech synthesis |
|
|
564 | (1) |
|
Spectral method for speech synthesis |
|
|
565 | (4) |
|
Waveform methods for speech synthesis |
|
|
569 | (2) |
|
|
|
571 | (1) |
|
|
|
571 | (1) |
|
Case Study: Automatic Unit Selection for Waveform Speech Synthesis |
|
|
572 | (3) |
|
|
|
575 | (2) |
|
|
|
577 | (2) |
|
Evaluation of Speech Synthesis Output |
|
|
579 | (1) |
|
|
|
580 | (1) |
| References |
|
581 | (39) |
| Index |
|
620 | |
Lisainfo e-raamatute kohta