( Modeling, criteria, optimization, implementation, and performance )

[1]  Jen-Tzung Chien,et al.  Joint acoustic and language modeling for speech recognition , 2010, Speech Commun..

[2]  Stanley F. Chen,et al.  A Gaussian Prior for Smoothing Maximum Entropy Models , 1999 .

[3]  Hervé Bourlard,et al.  Connectionist speech recognition , 1993 .

[4]  Georg Heigold,et al.  Modified MMI/MPE: a direct evaluation of the margin in speech recognition , 2008, ICML '08.

[5]  Wolfgang Macherey,et al.  Discriminative training and acoustic modeling for automatic speech recognition , 2010 .

[6]  Daniel Povey,et al.  Large scale discriminative training of hidden Markov models for speech recognition , 2002, Comput. Speech Lang..

[7]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[8]  Zhifei Li,et al.  First- and Second-Order Expectation Semirings with Applications to Minimum-Risk Training on Translation Forests , 2009, EMNLP.

[9]  Andreas Stolcke,et al.  Improved discriminative training using phone lattices , 2005, INTERSPEECH.

[10]  Eric Fosler-Lussier,et al.  CRANDEM: conditional random fields for word recognition , 2009, INTERSPEECH.

[11]  Daniel P. W. Ellis,et al.  Tandem connectionist feature extraction for conventional HMM systems , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[12]  Thomas Hofmann,et al.  Hidden Markov Support Vector Machines , 2003, ICML.

[13]  Dong Yu,et al.  Using continuous features in the maximum entropy model , 2009, Pattern Recognit. Lett..

[14]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[15]  Yan Yin,et al.  A fast optimization method for large margin estimation of HMMs based on second order cone programming , 2007, INTERSPEECH.

[16]  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 .

[17]  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.

[18]  Hermann Ney,et al.  On the Relationship between Classification Error Bounds and Training Criteria in Statistical Pattern Recognition , 2003, IbPRIA.

[19]  Brian Kingsbury,et al.  Boosted MMI for model and feature-space discriminative training , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[20]  Georg Heigold,et al.  Margin-Based Discriminative Training for String Recognition , 2010, IEEE Journal of Selected Topics in Signal Processing.

[21]  Michael I. Jordan,et al.  On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.

[22]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[23]  Mehryar Mohri,et al.  Weighted Automata Algorithms , 2009 .

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

[25]  Christian Igel,et al.  Empirical evaluation of the improved Rprop learning algorithms , 2003, Neurocomputing.

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

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

[28]  Georg Heigold,et al.  WFST Enabled Solutions to ASR Problems: Beyond HMM Decoding , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

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

[30]  H Hermansky,et al.  Perceptual linear predictive (PLP) analysis of speech. , 1990, The Journal of the Acoustical Society of America.

[31]  Lalit R. Bahl,et al.  Maximum mutual information estimation of hidden Markov model parameters for speech recognition , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[32]  Fernando Pereira,et al.  Efficient general lattice generation and rescoring , 1999, EUROSPEECH.

[33]  T. Bayes.  An essay towards solving a problem in the doctrine of chances , 2003 .

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

[35]  Shinji Watanabe,et al.  Discriminative training based on an integrated view of MPE and MMI in margin and error space , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[36]  Geoffrey Zweig,et al.  A segmental CRF approach to large vocabulary continuous speech recognition , 2009, 2009 IEEE Workshop on Automatic Speech Recognition & Understanding.

[37]  Mark J. F. Gales,et al.  Discriminative map for acoustic model adaptation , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[38]  Vaibhava Goel,et al.  Minimum Bayes-risk automatic speech recognition , 2000, Comput. Speech Lang..

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

[40]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[41]  Georg Heigold,et al.  A log-linear discriminative modeling framework for speech recognition , 2010 .

[42]  Hermann Ney,et al.  A word graph algorithm for large vocabulary continuous speech recognition , 1994, Comput. Speech Lang..

[43]  Steve Renals,et al.  Speech Recognition Using Augmented Conditional Random Fields , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

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

[45]  Hermann Ney,et al.  A convergence analysis of log-linear training and its application to speech recognition , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[46]  Georg Heigold,et al.  Equivalence of Generative and Log-Linear Models , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[47]  Lawrence K. Saul,et al.  Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[48]  Izhak Shafran,et al.  Learning a Discriminative Weighted Finite-State Transducer for Speech Recognition , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

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