Advances in neural information processing systems 2
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Hidden Markov models, maximum mutual information estimation, and the speech recognition problem
Hidden Markov models, maximum mutual information estimation, and the speech recognition problem
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical methods for speech recognition
Statistical methods for speech recognition
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
The acoustic-modeling problem in automatic speech recognition
The acoustic-modeling problem in automatic speech recognition
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Large-vocabulary speaker-independent continuous speech recognition: the sphinx system
Grafting: fast, incremental feature selection by gradient descent in function space
The Journal of Machine Learning Research
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Decision trees for phonological rules in continuous speech
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
IEICE - Transactions on Information and Systems
Buried Markov models for speech recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Maximum likelihood discriminant feature spaces
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Investigations into the Crandem approach to word recognition
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Pretreatment for speech machine translation
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
| Hi-index | 0.00 |
Acoustic modeling based on hidden Markov models (HMMs) is employed by state-of-the-art stochastic speech recognition systems. Although HMMs are a natural choice to warp the time axis and model the temporal phenomena in the speech signal, their conditional independence properties limit their ability to model spectral phenomena well. In this paper, a new acoustic modeling paradigm based on augmented conditional random fields (ACRFs) is investigated and developed. This paradigm addresses some limitations of HMMs while maintaining many of the aspects which have made them successful. In particular, the acoustic modeling problem is reformulated in a data driven, sparse, augmented space to increase discrimination. Acoustic context modeling is explicitly integrated to handle the sequential phenomena of the speech signal. We present an efficient framework for estimating these models that ensures scalability and generality. In the TIMIT phone recognition task, a phone error rate of 23.0% was recorded on the full test set, a significant improvement over comparable HMM-based systems.