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E-raamat: Robust Digital Processing of Speech Signals

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 06-Jun-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319536132
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Robust Digital Processing of Speech SignalsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 06-Jun-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319536132
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This book focuses on speech signal phenomena, presenting a robustification of the usual speech generation models with regard to the presumed types of excitation signals, which is equivalent to the introduction of a class of nonlinear models and the corresponding criterion functions for parameter estimation. Compared to the general class of nonlinear models, such as various neural networks, these models possess good properties of controlled complexity, the option of working in “online” mode, as well as a low information volume for efficient speech encoding and transmission. Providing comprehensive insights, the book is based on the authors’ research, which has already been published, supplemented by additional texts discussing general considerations of speech modeling, linear predictive analysis and robust parameter estimation.
1 Speech Signal Modeling
1(8)
1.1 Nature of Speech Signal
1(3)
1.2 Linear Model of Speech Signal
4(5)
2 Overview of Standard Methods
9(20)
2.1 Autocorrelation Method
11(1)
2.2 Covariant Method
12(3)
2.3 Forward and Backward Prediction
15(2)
2.4 Lattice Filter
17(2)
2.5 Method of Minimization of Forward Prediction Error
19(1)
2.6 Method of Minimization of Backward Prediction Error
19(1)
2.7 Method of Geometric Mean
20(1)
2.8 Method of Minimum
21(1)
2.9 General Method
21(1)
2.10 Method of Harmonic Mean
21(1)
2.11 Lattice-Covariant LP Method
22(3)
2.12 Basic Properties of Partial Correlation Coefficient
25(1)
2.13 Equivalence of Discrete Model and Linear Prediction Model
25(1)
2.14 Speech Synthesis Based on Linear Prediction Model
26(3)
3 Fundamentals of Robust Parameter Estimation
29(66)
3.1 Principles of Robust Parameter Estimation
29(6)
3.2 Robust Estimation of Signal Amplitude
35(5)
3.3 Fundamentals of Minimax Robust Estimation of Signal Amplitude
40(4)
3.4 Recursive Minimax Robust Algorithms for Signal Amplitude Estimation
44(7)
3.5 Statistical Models of Perturbations and Examples of Minimax Robust Estimator
51(10)
3.6 Practical Aspects of Implementation of Robust Estimators
61(4)
3.7 Robust Estimation of Parameters of Autoregressive Dynamic Signal Models
65(4)
3.8 Non-recursive Minimax Robust Estimation Algorithms
69(6)
3.9 Recursive Minimax Robust Estimation Algorithm
75(5)
3.10 Fundamentals of Robust Identification of Speech Signal Model
80(15)
Appendix 1 Analysis of Asymptotic Properties of Non-recursive Minimax Robust Estimation of Signal Amplitude
84(4)
Appendix 2 Analysis of Asymptotic Properties of Recursive Minimax Robust Estimation of Signal Amplitude
88(7)
4 Robust Non-recursive AR Analysis of Speech Signal
95(30)
4.1 Robust Estimations of Parameters of Linear Regression Model
96(3)
4.2 Non-recursive Robust Estimation Procedure: RBLP Method
99(6)
4.2.1 Newton Algorithm
100(1)
4.2.2 Dutter Algorithm
101(3)
4.2.3 Weighted Least Squares Algorithm
104(1)
4.3 Comparison of Robust and Non-robust Estimation Algorithms
105(6)
4.3.1 Analysis of the Estimation Error Variance
106(4)
4.3.2 Analysis of Estimation Shift
110(1)
4.4 Characteristics of M-Robust Estimation Procedure
111(2)
4.4.1 Model Validity
112(1)
4.4.2 Stability
112(1)
4.4.3 Computational Complexity
112(1)
4.5 Experimental Analysis
113(10)
4.5.1 Test Signals Obtained by Filtering Train of Dirac Pulses
113(3)
4.5.2 Test Signals Obtained by Filtering of Glottal Excitation
116(3)
4.5.3 Natural Speech Signal
119(4)
4.6 Discussion and Conclusion
123(2)
5 Robust Recursive AR Analysis of Speech Signal
125(30)
5.1 Linear Regression Model for Recursive Parameter Estimation
126(1)
5.2 Application of M-Estimation Robust Procedure: RRLS Method
127(2)
5.3 Robust Recursive Least-Squares Algorithm
129(3)
5.4 Adaptive Robust Recursive Estimation Algorithm
132(1)
5.5 Determination of Variable Forgetting Factor
133(3)
5.5.1 Approach Based on Discrimination Function
133(2)
5.5.2 Approach Based on Generalized Prediction Error
135(1)
5.6 Experimental Analysis on Test Sinusoids
136(9)
5.6.1 Testing with Fixed Forgetting Factor
137(1)
5.6.2 Testing with Variable Forgetting Factor
137(6)
5.6.3 Testing with Contaminated Additive Gaussian Noise
143(2)
5.7 Experimental Analysis of Speech Signals
145(8)
5.7.1 Test Signals Obtained by Filtering a Train of Dirac Pulses
146(1)
5.7.2 Test Signals Obtained by Filtering Glottal Excitation
147(2)
5.7.3 Natural Speech Signal
149(4)
5.8 Discussion and Conclusion
153(2)
6 Robust Estimation Based on Pattern Recognition
155(30)
6.1 Unsupervised Learning
156(7)
6.1.1 General Clustering Algorithms
157(1)
6.1.2 Frame-Based Methods
158(3)
6.1.3 Quadratic Classifier with Sliding Training Set
161(2)
6.2 Recursive Procedure Based on Pattern Recognition
163(7)
6.3 Application of Bhattacharyya Distance
170(4)
6.3.1 Bhattacharyya Distance
172(2)
6.4 Experimental Analysis
174(9)
6.4.1 Direct Evaluation
174(3)
6.4.2 Indirect Evaluation
177(6)
6.5 Conclusion
183(2)
7 Applications of Robust Estimators in Speech Signal Processing
185(28)
7.1 Segmentation of Speech Signal
186(9)
7.1.1 Basics of Modified Generalized Maximum Likelihood Algorithm
187(3)
7.1.2 Robust Discriminant Function
190(1)
7.1.3 Tests with Real Speech Signal
191(1)
7.1.4 Appendix 4: Robust MGLR Algorithm (RMGLR)
191(4)
7.2 Separation of Formant Trajectories
195(5)
7.2.1 Experimental Analysis
197(3)
7.3 CELP Coder of Speech Signal
200(13)
7.3.1 LSP Parameters
201(2)
7.3.2 Distance Measure
203(3)
7.3.3 Linear Prediction Methods with Sample Selection
206(1)
7.3.4 Experimental Analysis
207(6)
References 213(8)
Index 221
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