Sequence-discriminative training of recurrent neural networks
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Hermann Ney | Ralf Schlüter | Patrick Doetsch | Paul Voigtlaender | Simon Wiesler | H. Ney | Simon Wiesler | R. Schlüter | P. Voigtlaender | P. Doetsch
[1] Jürgen Schmidhuber,et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.
[2] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[3] 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.
[4] 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).
[5] T. Munich,et al. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.
[6] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[7] H. Ney,et al. INTERDEPENDENCE OF LANGUAGE MODELS AND DISCRIMINATIVE TRAINING , 2007 .
[8] Hervé Bourlard,et al. Connectionist Speech Recognition: A Hybrid Approach , 1993 .
[9] Jürgen Schmidhuber,et al. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.
[10] Hermann Ney,et al. Improvements in RWTH's System for Off-Line Handwriting Recognition , 2013, 2013 12th International Conference on Document Analysis and Recognition.
[11] Horst Bunke,et al. The IAM-database: an English sentence database for offline handwriting recognition , 2002, International Journal on Document Analysis and Recognition.
[12] Sepp Hochreiter,et al. The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[13] Haikal El Abed,et al. ICDAR 2009 Handwriting Recognition Competition , 2009, 2009 10th International Conference on Document Analysis and Recognition.
[14] Christopher Kermorvant,et al. Dropout Improves Recurrent Neural Networks for Handwriting Recognition , 2013, 2014 14th International Conference on Frontiers in Handwriting Recognition.
[15] Brian Kingsbury,et al. Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[16] Dong Yu,et al. Error back propagation for sequence training of Context-Dependent Deep NetworkS for conversational speech transcription , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[17] Georg Heigold,et al. Asynchronous stochastic optimization for sequence training of deep neural networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[18] Hermann Ney,et al. Open vocabulary handwriting recognition using combined word-level and character-level language models , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[19] Hermann Ney,et al. RASR - The RWTH Aachen University Open Source Speech Recognition Toolkit , 2011 .
[20] Georg Heigold,et al. Sequence discriminative distributed training of long short-term memory recurrent neural networks , 2014, INTERSPEECH.
[21] Lukás Burget,et al. Sequence-discriminative training of deep neural networks , 2013, INTERSPEECH.
[22] Hermann Ney,et al. Fast and Robust Training of Recurrent Neural Networks for Offline Handwriting Recognition , 2014, 2014 14th International Conference on Frontiers in Handwriting Recognition.
[23] Marcus Liwicki,et al. A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks , 2007 .
[24] Tara N. Sainath,et al. Scalable Minimum Bayes Risk Training of Deep Neural Network Acoustic Models Using Distributed Hessian-free Optimization , 2012, INTERSPEECH.
[25] Ronald J. Williams,et al. Gradient-based learning algorithms for recurrent networks and their computational complexity , 1995 .