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End-to-end (E2E) models have gained attention in the research field of automatic speech recognition (ASR).
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
1938
Earlier work this paper cites.
J. J. Godfrey, E. C. Holliman, and J. McDaniel, “Switchboard: telephone speech corpus for research and development,” in
1992
Earlier work this paper cites.
K. Maekawa, H. Koiso, S. Furui, and H. Isahara, “Spontaneous speech corpus of Japanese,” in
2000
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.
A. Rousseau, P. Deléglise, and Y. Estève, “Enhancing the TED-LIUM corpus with selected data for language modeling and more TED talks,” 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.
O. Vinyals, M. Fortunato, and N. Jaitly, “Pointer networks,” in
2015
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. Amodei
2016
Earlier work this paper cites.
R. Prabhavalkar, K. Rao, T. N. Sainath, B. Li, L. Johnson, and N. Jaitly, “A comparison of sequence-to-sequence models for speech recognition,” 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. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
Cited alongside, same era.
2017
Cited alongside, same era.
H. Bu, J. Du, X. Na, B. Wu, and H. Zheng, “Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline,” in
2017
2019
Later among the works it cites.
M. Stern, W. Chan, J. Kiros, and J. Uszkoreit, “Insertion transformer: Flexible sequence generation via insertion operations,” in
2019
Later among the works it cites.
J. Gu, C. Wang, and J. Zhao, “Levenshtein transformer,” in
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Gu, Q. Liu, and K. Cho, “Insertion-based decoding with automatically inferred generation order,”
2019
Later among the works it cites.
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Cited alongside, same era.
A. Zeyer, K. Irie, R. Schlüter, and H. Ney, “Improved training of end-to-end attention models for speech recognition,” in
2018
Cited alongside, same era.
S. Watanabe
2018
Cited alongside, same era.
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,” in
2019
Cited alongside, same era.
C. Lüscher, E. Beck, K. Irie, M. Kitza, W. Michel, A. Zeyer, R. Schlüter, and H. Ney, “RWTH ASR Systems for LibriSpeech: Hybrid vs Attention,” in
2019
Cited alongside, same era.
J. Salazar, K. Kirchhoff, and Z. Huang, “Self-attention networks for connectionist temporal classification in speech recognition,” in
2019
Later among the works it cites.
T. N. Sainath
2020
Closest in time.
2020
Closest in time.