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We present a state-of-the-art speech recognition system developed using end-to-end deep learning.
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D. R. Kincaid, T. C. Oppe, and D. M. Young · 1989
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
Connectionist Speech Recognition: A Hybrid Approach
H. Bourlard and N. Morgan · 1993
Earlier work this paper cites.
The Lombard reflex and its role on human listeners and automatic speech recognizers
J.-C. Junqua · 1993
Earlier work this paper cites.
Connectionist probability estimators in HMM speech recognition
S. Renals, N. Morgan, H. Bourlard, M. Cohen, and H. Franco · 1994
Earlier work this paper cites.
Bidirectional recurrent neural networks
M. Schuster and K. K. Paliwal · 1997
Earlier work this paper cites.
Size matters: An empirical study of neural network training for large vocabulary continuous speech recognition
D. Ellis and N. Morgan · 1999
Earlier work this paper cites.
The Fisher corpus: a resource for the next generations of speech-to-text
C. Cieri, D. Miller, and K. Walker · 2004
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, F. J. Huang, and L. Bottou · 2004
Earlier work this paper cites.
Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber · 2006
Earlier work this paper cites.
A fast data collection and augmentation procedure for object recognition
B. Sapp, A. Saxena, and A. Y. Ng · 2008
Earlier work this paper cites.
Unsupervised feature learning for audio classification using convolutional deep belief networks
H. Lee, P. Pham, Y. Largman, and A. Y. Ng · 2009
Earlier work this paper cites.
Large-scale deep unsupervised learning using graphics processors
R. Raina, A. Madhavan, and A. Ng · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
Flexible, high performance convolutional neural networks for image classification
D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber · 2011
Earlier work this paper cites.
Text detection and character recognition in scene images with unsupervised feature learning
A. Coates, B. Carpenter, C. Case, S. Satheesh, B. Suresh, T. Wang, D. J. Wu, and A. Y. Ng · 2011
Cited alongside, same era.
An analysis of single-layer networks in unsupervised feature learning
A. Coates, H. Lee, and A. Y. Ng · 2011
Cited alongside, same era.
Context-dependent pre-trained deep neural networks for large vocabulary speech recognition
G. Dahl, D. Yu, L. Deng, and A. Acero · 2011
Cited alongside, same era.
Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
Cited alongside, same era.
Acoustic modeling using deep belief networks
A. Mohamed, G. Dahl, and G. Hinton · 2011
Cited alongside, same era.
The Kaldi speech recognition toolkit
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, K. Veselý, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz, J. Silovsky, and G. Stemmer · 2011
Deep learning with COTS HPC
A. Coates, B. Huval, T. Wang, D. J. Wu, A. Y. Ng, and B. Catanzaro · 2013
Later among the works it cites.
Scalable modified Kneser-Ney language model estimation
K. Heafield, I. Pouzyrevsky, J. H. Clark, and P. Koehn · 2013
Later among the works it cites.
Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
Later among the works it cites.
Improvements to deep convolutional neural networks for LVCSR
T. Sainath, B. Kingsbury, A. Mohamed, G. Dahl, G. Saon, H. Soltau, T. Beran, A. Aravkin, and B. Ramabhadran · 2013
Later among the works it cites.
Deep convolutional neural networks for LVCSR
T. N. Sainath, A. rahman Mohamed, B. Kingsbury, and B. Ramabhadran · 2013
Later among the works it cites.
On the importance of momentum and initialization in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
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Feature engineering in context-dependent deep neural networks for conversational speech transcription
F. Seide, G. Li, X. Chen, and D. Yu · 2011
Cited alongside, same era.
Multi-column deep neural networks for image classification
D. C. Ciresan, U. Meier, and J. Schmidhuber · 2012
Cited alongside, same era.
Large scale distributed deep networks
J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Y. Ng · 2012
Cited alongside, same era.
Shift-invariance sparse coding for audio classification
R. Grosse, R. Raina, H. Kwong, and A. Y. Ng · 2012
Cited alongside, same era.
Deep neural networks for acoustic modeling in speech recognition
G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kingsbury · 2012
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Scalable minimum Bayes risk training of deep neural network acoustic models using distributed hessian-free optimization
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Cited alongside, same era.
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Sequence-discriminative training of deep neural networks
K. Vesely, A. Ghoshal, L. Burget, and D. Povey · 2013
Later among the works it cites.
Towards end-to-end speech recognition with recurrent neural networks
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First-pass large vocabulary continuous speech recognition using bi-directional recurrent DNNs
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Improving neural networks by preventing co-adaptation of feature detectors
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Joint training of convolutional and non-convolutional neural networks
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