Discriminative in Sequential Pattern Recognition

[1]  Xiaodong He,et al.  Discriminative Learning for Speech Recognition: Theory and Practice , 2008, Discriminative Learning for Speech Recognition.

[2]  Radford M. Neal,et al.  Haplotype inference using a Bayesian Hidden Markov model , 2007, Genetic epidemiology.

[3]  Dong Yu,et al.  Large-Margin Minimum Classification Error Training for Large-Scale Speech Recognition Tasks , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[4]  Scott Axelrod,et al.  Discriminative Estimation of Subspace Constrained Gaussian Mixture Models for Speech Recognition , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[5]  Jonathan Le Roux,et al.  Discriminative Training for Large-Vocabulary Speech Recognition Using Minimum Classification Error , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Yuqing Gao,et al.  Maximum entropy direct models for speech recognition , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Wu Chou,et al.  A Novel Learning Method for Hidden Markov Models in Speech and Audio Processing , 2006, 2006 IEEE Workshop on Multimedia Signal Processing.

[8]  Alex Acero,et al.  Joint Discriminative Front End and Back End Training for Improved Speech Recognition Accuracy , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[9]  Dong Yu,et al.  A Generative Modeling Framework for Structured Hidden Speech Dynamics , 2005 .

[10]  Yi Li,et al.  A generative/discriminative learning algorithm for image classification , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Alex Acero,et al.  Hidden conditional random fields for phone classification , 2005, INTERSPEECH.

[12]  Li Deng,et al.  Analysis and comparison of two speech feature extraction/compensation algorithms , 2005, IEEE Signal Processing Letters.

[13]  Geoffrey Zweig,et al.  fMPE: discriminatively trained features for speech recognition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[14]  Jonathan Le Roux,et al.  Optimization methods for discriminative training , 2005, INTERSPEECH.

[15]  Fernando Pereira.  Linear models for structure prediction , 2005, INTERSPEECH.

[16]  Hermann Ney,et al.  Investigations on error minimizing training criteria for discriminative training in automatic speech recognition , 2005, INTERSPEECH.

[17]  Jie Yang,et al.  A discriminative learning framework with pairwise constraints for video object classification , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[18]  Erik McDermott,et al.  Minimum classification error training of landmark models for real-time continuous speech recognition , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  Douglas D. O'Shaughnessy,et al.  Speech Processing , 2018 .

[20]  Mark J. F. Gales,et al.  MMI-MAP and MPE-MAP for acoustic model adaptation , 2003, INTERSPEECH.

[21]  Franz Josef Och,et al.  Minimum Error Rate Training in Statistical Machine Translation , 2003, ACL.

[22]  Wu Chou,et al.  minimum classification error linear regression for acoustic model adaptation of continuous density HMMS , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[23]  Wu Chou.  Minimum Classification Error (MCE) Approach in Pattern Recognition , 2003 .

[24]  Michael Collins,et al.  Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[25]  Daniel Povey,et al.  Minimum Phone Error and I-smoothing for improved discriminative training , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[26]  Alex Pentland,et al.  Discriminative, generative and imitative learning , 2002 .

[27]  William J. Byrne,et al.  Discriminative speaker adaptation with conditional maximum likelihood linear regression , 2001, INTERSPEECH.

[28]  Qiang Huo,et al.  A Discrete Contextual Stochastic Model for the Offline Recognition of Handwritten Chinese Characters , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[30]  Hermann Ney,et al.  Comparison of discriminative training criteria and optimization methods for speech recognition , 2001, Speech Commun..

[31]  Alex Acero,et al.  Spoken Language Processing , 2001 .

[32]  Ewan Birney,et al.  Hidden Markov models in biological sequence analysis , 2001, IBM J. Res. Dev..

[33]  Daniel Povey,et al.  Large scale discriminative training for speech recognition , 2000 .

[34]  Andrew McCallum,et al.  Maximum Entropy Markov Models for Information Extraction and Segmentation , 2000, ICML.

[35]  Alex Pentland,et al.  On Reversing Jensen's Inequality , 2000, NIPS.

[36]  Chin-Hui Lee,et al.  Minimum error rate training for PHMM-based text recognition , 1999, IEEE Trans. Image Process..

[37]  Li Deng,et al.  Speech trajectory discrimination using the minimum classification error learning , 1998, IEEE Trans. Speech Audio Process..

[38]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[39]  Durbin,et al.  Biological Sequence Analysis , 1998 .

[40]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[41]  Steve J. Young,et al.  MMIE training of large vocabulary recognition systems , 1997, Speech Communication.

[42]  Biing-Hwang Juang,et al.  Minimum classification error rate methods for speech recognition , 1997, IEEE Trans. Speech Audio Process..

[43]  S. Young,et al.  Lattice-based discriminative training for large vocabulary speech recognition , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[44]  Yves Normandin.  Maximum Mutual Information Estimation of Hidden Markov Models , 1996 .

[45]  Dimitri Kanevsky.  A generalization of the Baum algorithm to functions on non-linear manifolds , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[46]  Yangsheng Xu,et al.  Hidden Markov model approach to skill learning and its application to telerobotics , 1993, IEEE Trans. Robotics Autom..

[47]  Steve J. Young,et al.  MMI training for continuous phoneme recognition on the TIMIT database , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[48]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[49]  Biing-Hwang Juang,et al.  Discriminative learning for minimum error classification [pattern recognition] , 1992, IEEE Trans. Signal Process..

[50]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[51]  Yves Normandin,et al.  Hidden Markov models, maximum mutual information estimation, and the speech recognition problem , 1992 .

[52]  Dimitri Kanevsky,et al.  An inequality for rational functions with applications to some statistical estimation problems , 1991, IEEE Trans. Inf. Theory.

[53]  Michael Picheny,et al.  On a model-robust training method for speech recognition , 1988, IEEE Trans. Acoust. Speech Signal Process..

[54]  Scott E. Fahlman,et al.  An empirical study of learning speed in back-propagation networks , 1988 .

[55]  Peter F. Brown,et al.  The acoustic-modeling problem in automatic speech recognition , 1987 .

[56]  A. Nadas,et al.  A decision theorectic formulation of a training problem in speech recognition and a comparison of training by unconditional versus conditional maximum likelihood , 1983 .

[57]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[58]  L. Baum,et al.  Growth transformations for functions on manifolds. , 1968 .

[59]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..

[60]  L. Baum,et al.  An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .