Automatic Speech Recognition: A Deep Learning Approach


Automatic Speech Recognition: A Deep Learning Approach (2014) .. by Dong Yu, etc


Contents

1 Introduction … 1
1.1 Automatic Speech Recognition: A Bridge for Better Communication … 1
1.1.1 Human–Human Communication … 2
1.1.2 Human–Machine Communication … 2
1.2 Basic Architecture of ASR Systems … 4
1.3 Book Organization … 5
1.3.1 Part I: Conventional Acoustic Models … 6
1.3.2 Part II: Deep Neural Networks … 6
1.3.3 Part III: DNN-HMM Hybrid Systems for ASR… 7
1.3.4 Part IV: Representation Learning in Deep Neural Networks… 7
1.3.5 Part V: Advanced Deep Models … 7
References… 8

Part I Conventional Acoustic Models

2 Gaussian Mixture Models … 13
2.1 Random Variables … 13
2.2 Gaussian and Gaussian-Mixture Random Variables … 14
2.3 Parameter Estimation… 17
2.4 Mixture of Gaussians as a Model for the Distribution of Speech Features … 18
References… 20

3 Hidden Markov Models and the Variants … 23
3.1 Introduction … 23
3.2 Markov Chains… 25
3.3 Hidden Markov Sequences and Models … 26
3.3.1 Characterization of a Hidden Markov Model … 27
3.3.2 Simulation of a Hidden Markov Model … 29
3.3.3 Likelihood Evaluation of a Hidden Markov Model … 29
3.3.4 An Algorithm for Efficient Likelihood Evaluation … 30
3.3.5 Proofs of the Forward and Backward Recursions … 32
3.4 EM Algorithm and Its Application to Learning HMM Parameters … 33
3.4.1 Introduction to EM Algorithm … 33
3.4.2 Applying EM to Learning the HMM—Baum-Welch Algorithm … 35
3.5 Viterbi Algorithm for Decoding HMM State Sequences… 39
3.5.1 Dynamic Programming and Viterbi Algorithm … 39
3.5.2 Dynamic Programming for Decoding HMM States … 40
3.6 The HMM and Variants for Generative Speech Modeling and Recognition … 42
3.6.1 GMM-HMMs for Speech Modeling and Recognition … 43
3.6.2 Trajectory and Hidden Dynamic Models for Speech Modeling and Recognition… 44
3.6.3 The Speech Recognition Problem Using Generative Models of HMM and Its Variants… 46
References… 48

Part II Deep Neural Networks

4 Deep Neural Networks … 57
4.1 The Deep Neural Network Architecture … 57
4.2 Parameter Estimation with Error Backpropagation… 59
4.2.1 Training Criteria… 60
4.2.2 Training Algorithms … 61
4.3 Practical Considerations … 65
4.3.1 Data Preprocessing … 65
4.3.2 Model Initialization… 67
4.3.3 Weight Decay … 68
4.3.4 Dropout… 69
4.3.5 Batch Size Selection … 70
4.3.6 Sample Randomization … 72
4.3.7 Momentum … 73
4.3.8 Learning Rate and Stopping Criterion … 73
4.3.9 Network Architecture … 75
4.3.10 Reproducibility and Restartability … 75
References… 76

5 Advanced Model Initialization Techniques… 79
5.1 Restricted Boltzmann Machines … 79
5.1.1 Properties of RBMs … 81
5.1.2 RBM Parameter Learning … 83
5.2 Deep Belief Network Pretraining … 86
5.3 Pretraining with Denoising Autoencoder … 89
5.4 Discriminative Pretraining … 91
5.5 Hybrid Pretraining … 92
5.6 Dropout Pretraining… 93
References… 94

Part III Deep Neural Network-Hidden Markov Model Hybrid Systems for Automatic Speech Recognition

6 Deep Neural Network-Hidden Markov Model Hybrid Systems … 99
6.1 DNN-HMM Hybrid Systems … 99
6.1.1 Architecture … 99
6.1.2 Decoding with CD-DNN-HMM … 101
6.1.3 Training Procedure for CD-DNN-HMMs… 102
6.1.4 Effects of Contextual Window … 104
6.2 Key Components in the CD-DNN-HMM and Their Analysis … 106
6.2.1 Datasets and Baselines for Comparisons and Analysis … 106
6.2.2 Modeling Monophone States or Senones … 108
6.2.3 Deeper Is Better … 109
6.2.4 Exploit Neighboring Frames … 111
6.2.5 Pretraining… 111
6.2.6 Better Alignment Helps… 112
6.2.7 Tuning Transition Probability … 113
6.3 Kullback-Leibler Divergence-Based HMM… 113
References… 114

7 Training and Decoding Speedup … 117
7.1 Training Speedup … 117
7.1.1 Pipelined Backpropagation Using Multiple GPUs … 118
7.1.2 Asynchronous SGD … 121
7.1.3 Augmented Lagrangian Methods and Alternating Directions Method of Multipliers … 124
7.1.4 Reduce Model Size… 126
7.1.5 Other Approaches… 127
7.2 Decoding Speedup … 127
7.2.1 Parallel Computation… 128
7.2.2 Sparse Network … 130
7.2.3 Low-Rank Approximation … 132
7.2.4 Teach Small DNN with Large DNN … 133
7.2.5 Multiframe DNN … 134
References… 135

8 Deep Neural Network Sequence-Discriminative Training … 137
8.1 Sequence-Discriminative Training Criteria … 137
8.1.1 Maximum Mutual Information … 137
8.1.2 Boosted MMI … 139
8.1.3 MPE/sMBR … 140
8.1.4 A Uniformed Formulation … 141
8.2 Practical Considerations… 142
8.2.1 Lattice Generation … 142
8.2.2 Lattice Compensation … 143
8.2.3 Frame Smoothing … 145
8.2.4 Learning Rate Adjustment … 146
8.2.5 Training Criterion Selection … 146
8.2.6 Other Considerations… 147
8.3 Noise Contrastive Estimation … 147
8.3.1 Casting Probability Density Estimation Problem as a Classifier Design Problem … 148
8.3.2 Extension to Unnormalized Models… 150
8.3.3 Apply NCE in DNN Training … 151
References… 153

Part IV Representation Learning in Deep Neural Networks

9 Feature Representation Learning in Deep Neural Networks … 157
9.1 Joint Learning of Feature Representation and Classifier … 157
9.2 Feature Hierarchy … 159
9.3 Flexibility in Using Arbitrary Input Features … 162
9.4 Robustness of Features … 163
9.4.1 Robust to Speaker Variations … 163
9.4.2 Robust to Environment Variations … 165
9.5 Robustness Across All Conditions … 167
9.5.1 Robustness Across Noise Levels… 167
9.5.2 Robustness Across Speaking Rates … 169
9.6 Lack of Generalization Over Large Distortions … 170
References… 173

10 Fuse Deep Neural Network and Gaussian Mixture Model Systems … 177
10.1 Use DNN-Derived Features in GMM-HMM Systems … 177
10.1.1 GMM-HMM with Tandem and Bottleneck Features … 177
10.1.2 DNN-HMM Hybrid System Versus GMM-HMM System with DNN-Derived Features … 180
10.2 Fuse Recognition Results… 182
10.2.1 ROVER … 183
10.2.2 SCARF … 184
10.2.3 MBR Lattice Combination… 185
10.3 Fuse Frame-Level Acoustic Scores … 186
10.4 Multistream Speech Recognition… 187
References… 189

11 Adaptation of Deep Neural Networks … 193
11.1 The Adaptation Problem for Deep Neural Networks … 193
11.2 Linear Transformations … 194
11.2.1 Linear Input Networks … 195
11.2.2 Linear Output Networks … 196
11.3 Linear Hidden Networks … 198
11.4 Conservative Training … 199
11.4.1 L2 Regularization … 199
11.4.2 KL-Divergence Regularization … 200
11.4.3 Reducing Per-Speaker Footprint … 202
11.5 Subspace Methods … 204
11.5.1 Subspace Construction Through Principal Component Analysis … 204
11.5.2 Noise-Aware, Speaker-Aware, and Device-Aware Training … 205
11.5.3 Tensor… 209
11.6 Effectiveness of DNN Speaker Adaptation … 210
11.6.1 KL-Divergence Regularization Approach… 210
11.6.2 Speaker-Aware Training … 212
References… 213

Part V Advanced Deep Models

12 Representation Sharing and Transfer in Deep Neural Networks… 219
12.1 Multitask and Transfer Learning … 219
12.1.1 Multitask Learning … 219
12.1.2 Transfer Learning … 220
12.2 Multilingual and Crosslingual Speech Recognition … 221
12.2.1 Tandem/Bottleneck-Based Crosslingual Speech Recognition … 222
12.2.2 Shared-Hidden-Layer Multilingual DNN … 223
12.2.3 Crosslingual Model Transfer … 226
12.3 Multiobjective Training of Deep Neural Networks for Speech Recognition … 230
12.3.1 Robust Speech Recognition with Multitask Learning … 230
12.3.2 Improved Phone Recognition with Multitask Learning … 230
12.3.3 Recognizing both Phonemes and Graphemes … 231
12.4 Robust Speech Recognition Exploiting Audio-Visual Information … 232
References… 233

13 Recurrent Neural Networks and Related Models … 237
13.1 Introduction … 237
13.2 State-Space Formulation of the Basic Recurrent Neural Network … 239
13.3 The Backpropagation-Through-Time Learning Algorithm… 240
13.3.1 Objective Function for Minimization… 241
13.3.2 Recursive Computation of Error Terms … 241
13.3.3 Update of RNN Weights … 242
13.4 A Primal-Dual Technique for Learning Recurrent Neural Networks… 244
13.4.1 Difficulties in Learning RNNs … 244
13.4.2 Echo-State Property and Its Sufficient Condition … 245
13.4.3 Learning RNNs as a Constrained Optimization Problem … 245
13.4.4 A Primal-Dual Method for Learning RNNs … 246
13.5 Recurrent Neural Networks Incorporating LSTM Cells … 249
13.5.1 Motivations and Applications… 249
13.5.2 The Architecture of LSTM Cells … 250
13.5.3 Training the LSTM-RNN … 250
13.6 Analyzing Recurrent Neural Networks—A Contrastive Approach… 251
13.6.1 Direction of Information Flow: Top-Down versus Bottom-Up … 251
13.6.2 The Nature of Representations: Localist or Distributed … 254
13.6.3 Interpretability: Inferring Latent Layers versus End-to-End Learning… 255
13.6.4 Parameterization: Parsimonious Conditionals versus Massive Weight Matrices… 256
13.6.5 Methods of Model Learning: Variational Inference versus Gradient Descent … 258
13.6.6 Recognition Accuracy Comparisons … 258
13.7 Discussions … 259
References… 261

14 Computational Network … 267
14.1 Computational Network… 267
14.2 Forward Computation … 269
14.3 Model Training … 271
14.4 Typical Computation Nodes … 275
14.4.1 Computation Node Types with No Operand… 276
14.4.2 Computation Node Types with One Operand … 276
14.4.3 Computation Node Types with Two Operands … 281
14.4.4 Computation Node Types for Computing Statistics … 287
14.5 Convolutional Neural Network … 288
14.6 Recurrent Connections… 291
14.6.1 Sample by Sample Processing Only Within Loops … 292
14.6.2 Processing Multiple Utterances Simultaneously … 293
14.6.3 Building Arbitrary Recurrent Neural Networks … 293
References… 297

15 Summary and Future Directions … 299
15.1 Road Map … 299
15.1.1 Debut of DNNs for ASR… 299
15.1.2 Speedup of DNN Training and Decoding … 302
15.1.3 Sequence Discriminative Training… 302
15.1.4 Feature Processing … 303
15.1.5 Adaptation… 304
15.1.6 Multitask and Transfer Learning… 305
15.1.7 Convolution Neural Networks … 305
15.1.8 Recurrent Neural Networks and LSTM … 306
15.1.9 Other Deep Models… 306
15.2 State of the Art and Future Directions … 307
15.2.1 State of the Art—A Brief Analysis … 307
15.2.2 Future Directions … 308

References… 309
Index … 317

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