Fundamentals of statistical exponential families: with applications in statistical decision theory
Fundamentals of statistical exponential families: with applications in statistical decision theory
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Maximum conditional likelihood via bound maximization and the CEM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The acoustic-modeling problem in automatic speech recognition
The acoustic-modeling problem in automatic speech recognition
Discriminative, generative and imitative learning
Discriminative, generative and imitative learning
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
Discriminative Estimation of Subspace Constrained Gaussian Mixture Models for Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error
IEEE Transactions on Audio, Speech, and Language Processing
A Constrained Line Search Optimization Method for Discriminative Training of HMMs
IEEE Transactions on Audio, Speech, and Language Processing
Large margin hidden Markov models for speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Solving Large-Margin Hidden Markov Model Estimation via Semidefinite Programming
IEEE Transactions on Audio, Speech, and Language Processing
An inequality for rational functions with applications to some statistical estimation problems
IEEE Transactions on Information Theory
Minimum classification error learning for sequential data in the wavelet domain
Pattern Recognition
International Journal of Speech Technology
A comparative study of RPCL and MCE based discriminative training methods for LVCSR
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Journal of Signal Processing Systems
Minimum Classification Error Training Incorporating Automatic Loss Smoothness Determination
Journal of Signal Processing Systems
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Recently, discriminative training (DT) methods have achieved tremendous progress in automatic speech recognition (ASR). In this survey article, all mainstream DT methods in speech recognition are reviewed from both theoretical and practical perspectives. From the theoretical aspect, many effective discriminative learning criteria in ASR are first introduced and then a unifying view is presented to elucidate the relationship among these popular DT criteria originally proposed from different viewpoints. Next, some key optimization methods used to optimize these criteria are summarized and their convergence properties are discussed. Moreover, as some recent advances, a novel discriminative learning framework is introduced as a general scheme to formulate discriminative training of HMMs for ASR, from which a variety of new DT methods can be developed. In addition, some important implementation issues regarding how to conduct DT for large vocabulary ASR are also discussed from a more practical aspect, such as efficient implementation of discriminative training on word graphs and effective optimization of complex DT objective functions in high-dimensionality space, and so on. Finally, this paper is summarized and concluded with some possible future research directions for this area. As a technical survey, all DT techniques and ideas are reviewed and discussed in this paper from high level without involving too much technical detail and experimental result.