Fetching the paper…
Reading the bibliography…
The use of future contextual information is typically shown to be helpful for acoustic modeling.
A. W. M. Ieee, T. Hanazawa, G. Hinton, K. S. M. Ieee, and K. J. Lang, “Phoneme recognition using time-delay neural networks,”
1990
Earlier work this paper cites.
M. Schuster and K. K. Paliwal,
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber,
1997
Earlier work this paper cites.
G. F. A., J. Schmidhuber, and F. Cummins,
1999
Earlier work this paper cites.
F. A. Gers and J. Schmidhuber, “Recurrent nets that time and count,” in
2000
Earlier work this paper cites.
A. Graves, S. Fernández, and J. Schmidhuber,
2005
Earlier work this paper cites.
A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional lstm and other neural network architectures,”
2005
Earlier work this paper cites.
A. Graves and F. Gomez, “Connectionist temporal classification:labelling unsegmented sequence data with recurrent neural networks,” in
2006
Earlier work this paper cites.
G. E. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition,”
2012
Earlier work this paper cites.
K. Veselý, A. Ghoshal, L. Burget, and D. Povey, “Sequence-discriminative training of deep neural networks,”
2013
Earlier work this paper cites.
H. Sak, A. Senior, and F. Beaufays, “Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition,”
2014
Earlier work this paper cites.
A. Graves, N. Jaitly, and A. R. Mohamed, “Hybrid speech recognition with deep bidirectional lstm,” in
2014
Cited alongside, same era.
K. Cho, B. V. Merrienboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,”
2014
Cited alongside, same era.
J. Chung, C. Gulcehre, K. H. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,”
2014
Cited alongside, same era.
G. Saon, H. Soltau, D. Nahamoo, and M. Picheny, “Speaker adaptation of neural network acoustic models using i-vectors,” in
2014
Cited alongside, same era.
S. Zhang, C. Liu, H. Jiang, S. Wei, L. Dai, and Y. Hu, “Feedforward sequential memory networks: A new structure to learn long-term dependency,”
2015
Cited alongside, same era.
S. Zhang, H. Jiang, S. Xiong, S. Wei, and L. R. Dai, “Compact feedforward sequential memory networks for large vocabulary continuous speech recognition,” in
2016
Later among the works it cites.
A. Zeyer, R. Schlüter, and H. Ney, “Towards online-recognition with deep bidirectional lstm acoustic models,” in
2016
Later among the works it cites.
K. Chen and Q. Huo,
2016
Later among the works it cites.
K. Chen, Z. J. Yan, and Q. Huo, “A context-sensitive-chunk bptt approach to training deep lstm/blstm recurrent neural networks for offline handwriting recognition,” in
2016
Later among the works it cites.
D. Povey, V. Peddinti, D. Galvez, P. Ghahremani, V. Manohar, X. Na, Y. Wang, and S. Khudanpur, “Purely sequence-trained neural networks for asr based on lattice-free mmi,” in
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
V. Peddinti, D. Povey, and S. Khudanpur, “A time delay neural network architecture for efficient modeling of long temporal contexts,” in
2015
Cited alongside, same era.
Y. Zhang, G. Chen, D. Yu, K. Yao, S. Khudanpur, and J. Glass, “Highway long short-term memory rnns for distant speech recognition,”
2015
Cited alongside, same era.
D. Amodei, R. Anubhai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, J. Chen, M. Chrzanowski, A. Coates, and G. Diamos, “Deep speech 2: End-to-end speech recognition in english and mandarin,” in
2015
Cited alongside, same era.
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio augmentation for speech recognition,”
2015
Cited alongside, same era.
2015
Cited alongside, same era.
V. Peddinti, Y. Wang, D. Povey, and S. Khudanpur, “Low latency acoustic modeling using temporal convolution and lstms,”
2017
Later among the works it cites.
S. Xue and Z. Yan, “Improving latency-controlled blstm acoustic models for online speech recognition,” in
2017
Later among the works it cites.
M. Ravanelli, P. Brakel, M. Omologo, and Y. Bengio, “Improving speech recognition by revising gated recurrent units,”
2017
Later among the works it cites.
T. Masuko, “Computational cost reduction of long short-term memory based on simultaneous compression of input and hidden state,” in
2017
Later among the works it cites.
H. Bu, J. Du, X. Na, B. Wu, and H. Zheng, “Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline,” 2017
2017
Later among the works it cites.