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We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition.
L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,”
1989
Earlier work this paper cites.
H. Bourlard and N. Morgan,
1994
Earlier work this paper cites.
N. Morgan, H. Bourlard, S. Greenberg, and H. Hermansky, “Stochastic perceptual auditory-event-based models for speech recognition,” in
1994
Earlier work this paper cites.
A. Senior and A. Robinson, “Forward-backward retraining of recurrent neural networks,” in
1994
Earlier work this paper cites.
S. Young, J. Odell, and P. Woodland, “Tree-based state tying for high accuracy acoustic modelling,” in
1994
Earlier work this paper cites.
A. Robinson, M. Hochberg, and S. Renals, “The use of recurrent networks in continuous speech recognition,” in
1996
Earlier work this paper cites.
M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,”
1997
Earlier work this paper cites.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” in
2006
Earlier work this paper cites.
S. Fernández, A. Graves, and J. Schmidhuber, “An application of recurrent neural networks to discriminative keyword spotting,” in
2007
Cited alongside, same era.
F. Eyben, M. Wollmer, B. Schuller, and A. Graves, “From speech to letters using a novel neural network architecture for grapheme based ASR,” in
2009
Cited alongside, same era.
B. Kingsbury, “Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling,” in
2009
Cited alongside, same era.
Q. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. Corrado, J. Dean, and A. Ng, “Building high-level features using large scale unsupervised learning,” in
2012
Cited alongside, same era.
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. Le, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Ng, “Large scale distributed deep networks,” in
2012
H. Su, G. Li, D. Yu, and F. Seide, “Error back propagation for sequence training of context-dependent deep networks for conversational speech transcription,” in
2013
Later among the works it cites.
G. Heigold, V. Vanhoucke, A. Senior, P. Nguyen, M. Ranzato, M. Devin, and J. Dean, “Multilingual acoustic models using distributed deep neural networks,” in
2013
Later among the works it cites.
K. Veselý, A. Ghoshal, L. Burget, and D. Povey, “Sequence-discriminative training of deep neural networks,” in
2013
Later among the works it cites.
H. Sak, A. Senior, and F. Beaufays, “Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling,” in
2014
Later among the works it cites.
H. Sak, O. Vinyals, G. Heigold, A. Senior, E. McDermott, R. Monga, and M. Mao, “Sequence discriminative distributed training of long short-term memory recurrent neural networks,” in
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Cited alongside, same era.
B. Kingsbury, T. N. Sainath, and H. Soltau, “Scalable minimum Bayes risk training of deep neural network acoustic models using distributed Hessian-free optimization,” in
2012
Cited alongside, same era.
A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in
2013
Cited alongside, same era.
A. Graves, N. Jaitly, and A. Mohamed, “Hybrid speech recognition with deep bidirectional LSTM,” in
2013
Cited alongside, same era.
2014
Later among the works it cites.
A. Senior, G. Heigold, M. Bacchiani, and H. Liao, “GMM-free DNN training,” in
2014
Later among the works it cites.
H. Sak, A. Senior, K. Rao, O. Irsoy, A. Graves, F. Beaufays, and J. Schalkwyk, “Learning acoustic frame labeling for speech recognition with recurrent neural networks,” in
2015
Closest in time.
A. Senior, H. Sak, and I. Shafran, “Context dependent phone models for LSTM RNN acoustic modelling,” in
2015
Closest in time.