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Transformer has achieved competitive performance against state-of-the-art end-to-end models in automatic speech recognition (ASR), and requires significantly less training time than RNN-based models.
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.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz
2011
Earlier work this paper cites.
A. Graves, “Sequence transduction with recurrent neural networks,” in
2012
Earlier work this paper cites.
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.
T. Ko, V. Peddinti, D. Povey, and S. Khudanpur, “Audio augmentation for speech recognition,” in
2015
Earlier work this paper cites.
G. Pundak and T. N. Sainath, “Lower frame rate neural network acoustic models,” in
2016
Earlier work this paper 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
Earlier work this paper cites.
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
Earlier work this paper cites.
V. Peddinti, Y. Wang, D. Povey, and S. Khudanpur, “Low latency acoustic modeling using temporal convolution and LSTMs,”
2017
Earlier work this paper cites.
L. Dong, S. Xu, and B. Xu, “Speech-transformer: A no-recurrence sequence-to-sequence model for speech recognition,” in
2018
Earlier work this paper cites.
C.-C. Chiu and C. Raffel, “Monotonic chunkwise attention,” in
2018
Cited alongside, same era.
2018
Cited alongside, same era.
P. Shaw, J. Uszkoreit, and A. Vaswani, “Self-attention with relative position representations,” in
2018
Cited alongside, same era.
T. Kudo and J. Richardson, “SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing,” in
2018
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in
2019
Cited alongside, same era.
Z. Tian, J. Yi, J. Tao, Y. Bai, and Z. Wen, “Self-attention transducers for end-to-end speech recognition,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
R. Al-Rfou, D. Choe, N. Constant, M. Guo, and L. Jones, “Character-level language modeling with deeper self-attention,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
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Z. Dai, Z. Yang, Y. Yang, J. G. Carbonell, Q. Le, and R. Salakhutdinov, “Transformer-XL: Attentive language models beyond a fixed-length context,” in
2019
Cited alongside, same era.
S. Karita, N. E. Y. Soplin, S. Watanabe, M. Delcroix, A. Ogawa, and T. Nakatani, “Improving transformer-based end-to-end speech recognition with connectionist temporal classification and language model integration,” in
2019
Cited alongside, same era.
A. Zeyer, P. Bahar, K. Irie, R. Schlüter, and H. Ney, “A comparison of transformer and lstm encoder decoder models for asr,” in
2019
Cited alongside, same era.
S. Karita, N. Chen, T. Hayashi, T. Hori, H. Inaguma, Z. Jiang, M. Someki, N. E. Y. Soplin, R. Yamamoto, X. Wang, S. Watanabe, T. Yoshimura, and W. Zhang, “A comparative study on transformer vs RNN in speech applications,” in
2019
Cited alongside, same era.
N. Moritz, T. Hori, and J. Le Roux, “Triggered attention for end-to-end speech recognition,” in
2019
Cited alongside, same era.
Y. He, T. N. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y. Wu, R. Pang
2019
Cited alongside, same era.
T. N. Sainath, Y. He, B. Li, A. Narayanan, R. Pang, A. Bruguier, S. Chang, W. Li, R. Alvarez, Z. Chen, C. Chiu, D. Garcia, A. Gruenstein, K. Hu, A. Kannan, Q. Liang, I. McGraw, C. Peyser, R. Prabhavalkar, G. Pundak, D. Rybach, Y. Shangguan, Y. Sheth, T. Strohman, M. Visontai, Y. Wu, Y. Zhang, and D. Zhao, “A streaming on-device end-to-end model surpassing server-side conventional model quality and latency,” in
2020
Closest in time.
Q. Zhang, H. Lu, H. Sak, A. Tripathi, E. McDermott, S. Koo, and S. Kumar, “Transformer Transducer: A streamable speech recognition model with transformer encoders and RNN-T loss,” in
2020
Closest in time.
Y. Wang, A. Mohamed, D. Le, C. Liu, A. Xiao, J. Mahadeokar, H. Huang, A. Tjandra, X. Zhang, F. Zhang, C. Fuegen, G. Zweig, and M. L. Seltzer, “Transformer-based acoustic modeling for hybrid speech recognition,” in
2020
Closest in time.
D. S. Park, Y. Zhang, C. Chiu, Y. Chen, B. Li, W. Chan, Q. V. Le, and Y. Wu, “SpecAugment on large scale datasets,” in
2020
Closest in time.
“Common Voice,” https://voice.mozilla.org, accessed: 2020-05-15
2020
Closest in time.