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We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurrent neural network decoder that directly emits a stream of phonemes.
Global optimization of a neural network-hidden Markov model hybrid
Bengio, Y., De Mori · 1992
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Connectionist speech recognition: a hybrid approach
Bourlard, H. A. and Morgan, N. (1994) · 1994
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Neural Networks for Speech and Sequence Recognition
Bengio, Y. (1996) · 1996
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Bidirectional recurrent neural networks
Schuster, M. and Paliwal, K. K. (1997) · 1997
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
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Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks
Graves, A., Fernández, S., Gomez, F., and Schmidhuber, J. (2006) · 2006
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Discriminative learning in sequential pattern recognition
He, X., Deng, L., and Chou, W. (2008) · 2008
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Deep belief networks for phone recognition
A Mohamed, Dahl, G., and Hinton, G. (2009) · 2009
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Lattice-based optimization of sequence classification criteria for neural-network acoustic modeling
Kingsbury, B. (2009) · 2009
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Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y. (2010) · 2010
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The kaldi speech recognition toolkit
Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., and others (2011) · 2011
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Sequence transduction with recurrent neural networks
Graves, A. (2012) · 2012
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ADADELTA: An adaptive learning rate method
Zeiler, M. D. (2012) · 2012
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Hybrid speech recognition with deep bidirectional LSTM
Graves, A., Jaitly, N., and Mohamed, A.-r. (2013a) · 2013
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Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., and Hinton, G. (2013b) · 2013
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On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y. (2013) · 2013
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Sequence-discriminative training of deep neural networks
Veselỳ, K., Ghoshal, A., Burget, L., and Povey, D. (2013) · 2013
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y. (2014) · 2014
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
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Maxout networks
Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013a) · 2013
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Generating sequences with recurrent neural networks
Graves, A. (2013) · 2013
Cited alongside, same era.
Pylearn2: a machine learning research library
Goodfellow, I. J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., and Bengio, Y. (2013b)
Cited in the paper.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., and Kingsbury, B. (2012a)
Cited in the paper.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R. (2012b)
Cited in the paper.
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014) · 2014
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Deep convolutional neural networks for large-scale speech tasks
Sainath, T. N., Kingsbury, B., Saon, G., Soltau, H., Mohamed, A.-r., Dahl, G., and Ramabhadran, B. (2014) · 2014
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