Hybrid MLP/HMM acoustic models

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Table of contents

Acknowledgments
Abstract
Résumé
List of Figures
List of Tables
List of Abbreviations
I. Context of this work
1. Introduction
1.1. The statistical framework
1.2. Cost efficient training data production
1.3. Matching the target data
1.4. Scientific goals
1.5. Outline
2. Automatic speech recognition
2.1. Statistical speech recognition
2.2. Acoustic modeling
2.2.1. Hidden Markov models
2.2.2. Parameter estimation
2.2.3. Model structure
2.2.4. Feature extraction
2.2.5. Hybrid MLP/HMM acoustic models
2.3. Language modeling
2.3.1. Definition
2.3.2. N-gram based language models
2.3.3. Other language modeling approaches
2.4. Decoding
2.4.1. Word recognizer
2.4.2. Evaluation measures
3. Baseline Automatic Speech Recognition System
3.1. The LIMSI broadcast recognition system
3.1.1. Acoustic modeling
3.1.2. Language modeling
3.1.3. Decoding
3.2. The European Portuguese system
3.2.1. Corpora
3.2.2. Pronunciation modeling
3.2.3. Early system
3.2.4. Improved system
3.3. The English system
3.3.1. Corpora
3.3.2. System overview
3.4. Summary
II. Unsupervised model training
4. Unsupervised HMM/GMM-based acoustic model training
4.1. Introduction
4.1.1. Maximum likelihood estimation
4.1.2. Maximum likelihood estimation without audio transcriptions
4.2. Approximating with the 1-best hypothesis
4.2.1. The influence of recognition errors
4.2.2. Generalizing the unsupervised training algorithm
4.2.3. Confidence measures
4.2.4. Weighting by confidence measures
4.2.5. Filtering by confidence measures
4.3. Approximating with multiple decoding hypotheses
4.3.1. The lattice-based training approach
4.3.2. Influence of the decoding parameters
4.3.3. Comparing lattice-based and 1-best training approaches
4.3.4. Comparing lattice-based, 1-best weighting and 1-best filtering approaches
4.4. Other training strategies
4.5. Summary
5. Unsupervised multi-layer perceptron training
5.1. Introduction
5.2. Multi-layer perceptron neural networks
5.2.1. MLP training
5.2.2. Hybrid HMM/MLP architecture
5.2.3. MLP for feature extraction
5.3. Unsupervised bottleneck MLP training
5.3.1. Experimental setup
5.3.2. Comparing PLP and MLP based acoustic models
5.3.3. Comparing unsupervised and supervised MLP and HMM training approaches
5.3.4. Filtering by confidence measures
5.3.5. Using additional untranscribed acoustic data
5.3.6. Comparing unsupervised and cross-lingual MLPs
5.4. Summary
6. Unsupervised language model training
6.1. Introduction
6.2. Unsupervised backoff n-gram language model training
6.2.1. Filtering by confidence measures
6.2.2. Weighting by confidence measures
6.2.3. Using multiple decoding hypotheses
6.2.4. Kneser-Ney smoothing with fractional counts
6.2.5. Experiments
6.3. Unsupervised neural network language model adaptation
6.3.1. Structured output layer neural network language model
6.3.2. Experiments
6.4. Summary
III. Model combination
7. Acoustic model interpolation
7.1. Introduction
7.2. Acoustic model adaptation
7.2.1. Maximum a posteriori
7.2.2. Maximum likelihood linear regression
7.2.3. Constrained maximum likelihood linear regression
7.2.4. Adaptive training approaches
7.3. Interpolating models
7.3.1. Interpolation of Gaussian mixture models
7.3.2. Data weighting
7.3.3. Estimation of the interpolation coefficients
7.4. Gaussian mixture model reduction
7.4.1. Gaussian mixture model reduction strategies
7.4.2. Constrained maximum likelihood estimation
7.5. Summary
8. Experiments with acoustic model interpolation
8.1. Introduction
8.2. Gaussian mixture reduction algorithm
8.2.1. Reduction of HMM state mixtures
8.2.2. Estimating universal background models
8.3. European Portuguese broadcast data recognition
8.3.1. Impact of interpolation coefficients
8.3.2. Comparing interpolation, data weighting, pooling and MAP adaptation
8.4. Multiple accented English data recognition
8.4.1. Experimental setup
8.4.2. Accent-adaptation via model interpolation
8.4.3. On-the-fly acoustic model interpolation
8.4.4. Leaving target accent out
8.5. Summary
IV. Conclusions
Publications by the author
Bibliography