Hidden Markov models, maximum mutual information estimation, and the speech recognition problem
Hidden Markov models, maximum mutual information estimation, and the speech recognition problem
The nature of statistical learning theory
The nature of statistical learning theory
Investigation of silicon auditory models and generalization of linear discriminant analysis for improved speech recognition
Maximum likelihood discriminant feature spaces
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Large-Margin Discriminative Training of Hidden Markov Models for Speech Recognition
ICSC '07 Proceedings of the International Conference on Semantic Computing
Large-margin minimum classification error training: A theoretical risk minimization perspective
Computer Speech and Language
Empirical error rate minimization based linear discriminant analysis
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Soft margin estimation for automatic speech recognition
Soft margin estimation for automatic speech recognition
Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error
IEEE Transactions on Audio, Speech, and Language Processing
Large margin hidden Markov models for speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Approximate Test Risk Bound Minimization Through Soft Margin Estimation
IEEE Transactions on Audio, Speech, and Language Processing
Leveraging relevance cues for language modeling in speech recognition
Information Processing and Management: an International Journal
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This paper considers training data selection for discriminative training of acoustic models for large vocabulary continuous speech recognition (LVCSR). Three novel data selection approaches are proposed. First, the average phone accuracy over all hypothesized word sequences in the word lattice of a training utterance is utilized for utterance-level data selection. Second, phone-level data selection based on the difference between the expected accuracy of a phone arc and the average phone accuracy of the word lattice is investigated. Finally, frame-level data selection based on the normalized frame-level entropy of Gaussian posterior probabilities obtained from the word lattice is explored. The underlying characteristics of the presented approaches are extensively investigated and their performance is verified by comparison with standard discriminative training approaches. Experiments conducted on a broadcast news speech transcription task show that with the aid of phone- and frame-level data selection we can reduce more than half of the turnaround time for acoustic model training and simultaneously obtain a comparably good set of discriminative acoustic models.