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The attention-based Transformer model has achieved promising results for speech recognition (SR) in the offline mode.
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.
A. Graves, “Sequence transduction with recurrent neural networks,”
2012
Earlier work this paper cites.
A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in
2013
Earlier work this paper cites.
A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
P. Ghahremani, B. BabaAli, D. Povey, K. Riedhammer, J. Trmal, and S. Khudanpur, “A pitch extraction algorithm tuned for automatic speech recognition,” in
2014
Earlier work this paper cites.
J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” in
2015
Earlier work this paper cites.
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: An ASR corpus based on public domain audio books,” in
2015
Earlier work this paper cites.
D. Amodei, S. Ananthanarayanan, R. Anubhai, J. Bai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, Q. Cheng, G. Chen
2016
Earlier work this paper cites.
W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in
2016
Earlier work this paper cites.
D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio, “End-to-end attention-based large vocabulary speech recognition,” in
2016
Earlier work this paper cites.
W. Chan and I. Lane, “On online attention-based speech recognition and joint mandarin character-pinyin training,” in
2016
Earlier work this paper cites.
K. Rao, H. Sak, and R. Prabhavalkar, “Exploring architectures, data and units for streaming end-to-end speech recognition with rnn-transducer,” in
2017
Cited alongside, same era.
S. Watanabe, T. Hori, S. Kim, J. R. Hershey, and T. Hayashi, “Hybrid ctc/attention architecture for end-to-end speech recognition,”
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
Cited alongside, same era.
M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner: Trainable text-speech alignment using kaldi.” in
2017
Cited alongside, same era.
C.-C. Chiu, T. N. Sainath, Y. Wu, R. Prabhavalkar, P. Nguyen, Z. Chen, A. Kannan, R. J. Weiss, K. Rao, E. Gonina
2018
Cited alongside, same era.
2019
Later among the works it cites.
Y. Wang, A. Mohamed, D. Le, C. Liu, A. Xiao, J. Mahadeokar, H. Huang, A. Tjandra, X. Zhang, F. Zhang
2019
Later among the works it cites.
Z. Dai, Z. Yang, Y. Yang, J. G. Carbonell, Q. V. Le, and R. Salakhutdinov, “Transformer-xl: Attentive language models beyond a fixed-length context,” in
2019
Later among the works it cites.
E. Tsunoo, Y. Kashiwagi, T. Kumakura, and S. Watanabe, “Transformer ASR with contextual block processing,” in
2019
Later among the works it cites.
N. Moritz, T. Hori, and J. L. Roux, “Triggered attention for end-to-end speech recognition,” in
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C. Chiu and C. Raffel, “Monotonic chunkwise attention,” in
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Cited alongside, same era.
L. Dong, F. Wang, and B. Xu, “Self-attention aligner: A latency-control end-to-end model for ASR using self-attention network and chunk-hopping,” in
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
D. S. Park, W. Chan, Y. Zhang, C.-C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le, “Specaugment: A simple data augmentation method for automatic speech recognition,”
2019
Later among the works it cites.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.