Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27, 2009, Revised Selected

✍ Scribed by Jordi Sole-Casals, Vladimir Zaiats

• Publisher: Springer
• Year: 2010
• Tongue: English
• Leaves: 209
• Series: Lecture Notes in Computer Science - Lecture Notes Artificial Intelligence
• Category: Library

✦ Synopsis

This book constitutes the proceedings of the International Conference on Advances in Nonlinear Speech Processing, NOLISP 2009, held in Vic (Barcelona), Spain during June 25-27, 2009. The volume deals with new ideas, techniques and results related to alternative approaches in speech processing.

✦ Table of Contents

364211508X......Page 1
Lecture Notes in Artificial Intelligence 5933......Page 2
Advances in Nonlinear
Speech Processing......Page 3
Preface......Page 5
Table of Contents......Page 8
Multimodality of Speech......Page 11
From Audiovisual Speech Enhancement to Audiovisual Source Separation......Page 12
Audiovisual Source Separation......Page 13
Intelligent Room......Page 15
Conclusion......Page 19
Introduction......Page 22
General Formulation......Page 23
Inference Algorithms and Results......Page 24
Conclusion......Page 25
Introduction......Page 27
V1 Visual Cortex Model......Page 28
V1 Normalized Components Are Approximately Independent......Page 30
Mutual Information Measures......Page 32
Marginal and Conditional PDFs......Page 33
Conclusions......Page 34
Introduction......Page 36
Mixed Excitation......Page 37
Evaluation of Speech Database......Page 38
Adaptation of Style Models......Page 39
Objective Evaluation......Page 40
References......Page 43
Introduction......Page 45
Chirp Decomposition of Speech......Page 46
Results on Synthetic Speech......Page 48
Results on Real Speech......Page 49
Conclusion......Page 51
Introduction......Page 53
Fundamental Frequency (F0)......Page 55
Shift-Difference Amplitude (SDA)......Page 56
Power Peak Rising and Falling Degrees (PWP)......Page 57
Classification......Page 58
References......Page 59
Introduction......Page 61
The Hartley Phase Spectrum......Page 62
Applications of the Hartley Phase Spectrum......Page 63
Spectrograms’ Implementation and Feature Selection......Page 64
Spectrogram Selection and Classification Scheme......Page 65
Experimental Results and Discussion......Page 66
Additional Classification Results and Discussion......Page 67
References......Page 68
Introduction......Page 70
A Gaussian Mixture Model for Noise......Page 71
Derivation of MMSE Clean Speech Estimators Using Gaussian Mixture Priors......Page 73
Experimental Results......Page 75
Conclusions......Page 76
Introduction......Page 78
Keyword Spotter Components......Page 79
Architecture......Page 80
Experiments......Page 83
Conclusion......Page 84
Introduction......Page 86
Entropy......Page 87
Other Functions......Page 88
Experiments......Page 89
Conclusions......Page 91
References......Page 92
Introduction......Page 94
Image Processing Applications......Page 95
Speech Processing Applications......Page 96
References......Page 98
Ataxia SCA2 Incidence......Page 100
Independent Component Analysis Applied to EOG Data......Page 101
Experimental Results and Discussion......Page 102
Conclusion......Page 103
References......Page 104
Introduction......Page 105
Multi-scale Product......Page 106
Spectral Multi-scale Product for Pitch Estimation......Page 108
Evaluation......Page 110
References......Page 111
Introduction......Page 113
Time-Frequency Analysis......Page 114
Continuity Constraint......Page 115
Formant Track Labelling......Page 116
Results and Discussion......Page 117
Conclusion......Page 119
References......Page 120
Introduction......Page 121
Translation-Invariant Transformations of Class CT......Page 123
Translation-Invariant Feature Candidates for ASR......Page 124
Experimental Setup......Page 125
Results and Discussion......Page 126
Conclusions......Page 128
Introduction......Page 130
Feature Extraction......Page 131
Classification......Page 132
Experimental Results......Page 133
Fusion......Page 134
Conclusions......Page 135
Introduction......Page 138
Best Basis Algorithm......Page 139
Mean Best Basis Algorithm......Page 140
Phoneme Recognition......Page 142
Conclusions......Page 143
References......Page 144
Introduction......Page 146
Auditory Filters Modelling......Page 147
Perceptual Based Spectral Subtraction......Page 148
Results and Evaluation......Page 150
Conclusion......Page 152
References......Page 153
Introduction......Page 154
Discrete Wavelet Transform Analysis......Page 155
Quantization of Wavelet Coefficients at All the Stages, Midtread vs. Midrise......Page 156
Proposed Method......Page 157
Experiments......Page 158
Conclusions......Page 160
References......Page 161
Introduction......Page 162
Discriminative Common Vector......Page 164
Principal Component Analysis......Page 165
Classification System......Page 166
Artificial Neural Network......Page 167
Experiments and Results......Page 168
References......Page 171
Introduction......Page 173
The Mathematical Morphology......Page 174
The Selected Pitch Tracking Algorithm......Page 177
Signal Preprocessing Based on Mathematical Morphology......Page 178
References......Page 180
State Representation of the Problem......Page 181
The Linear Gaussian Component Estimation......Page 183
Likelihood Ratio......Page 184
Simulations and Results......Page 185
Conclusion......Page 186
References......Page 187
Introduction......Page 188
Integrals Involving Cyclic Products of Kernels......Page 189
Cumulants of General Bilinear Forms of Gaussian Random Vectors......Page 190
Applications......Page 191
Introduction......Page 194
Basic GHSOM Model......Page 195
The Proposed Competitive Learning Algorithm......Page 196
Experimental Results......Page 197
References......Page 200
Introduction......Page 201
Proposed Technique......Page 202
Feature Extraction......Page 203
Ensemble Classifiers-Random Forests......Page 204
Experimental Setup......Page 205
Discussion......Page 206
Future Work......Page 207
References......Page 208
Author Index......Page 209