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D. B. Paul and J. M. Baker, “The design for the wall street journal-based CSR corpus,” in ICSLP , 1992
1992
Earlier work this paper cites.
J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, and D. S. Pallett, “Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1,” NASA STI/Recon technical report n , vol. 93, 1993
1993
Earlier work this paper cites.
C. Busso, M. Bulut, C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, “IEMOCAP: interactive emotional dyadic motion capture database,” Lang. Resour. Evaluation , vol. 42, 2008
2008
Earlier work this paper cites.
2010
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 LREC , 2014
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Ravanelli, L. Cristoforetti, R. Gretter, M. Pellin, A. Sosi, and M. Omologo, “The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments,” in ASRU , 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in ICLR , 2015
2015
Earlier work this paper cites.
K. J. Piczak, “ESC: dataset for environmental sound classification,” in ACMMM , 2015
2015
Earlier work this paper cites.
R. Xia and Y. Liu, “Leveraging valence and activation information via multi-task learning for categorical emotion recognition,” in ICASSP , 2015
2015
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 NIPS , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” in Interspeech , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Y. Chung, W. Hsu, H. Tang, and J. R. Glass, “An unsupervised autoregressive model for speech representation learning,” in Interspeech , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Pascual, M. Ravanelli, J. Serrà, A. Bonafonte, and Y. Bengio, “Learning problem-agnostic speech representations from multiple self-supervised tasks,” in Interspeech , 2019
2019
Cited alongside, same era.
Y. A. Chung and J. R. Glass, “Improved speech representations with multi-target autoregressive predictive coding,” in ACL , 2020
2020
Closest in time.
A. T. Liu, S. Yang, P. Chi, P. Hsu, and H. Lee, “Mockingjay: Unsupervised speech representation learning with deep bidirectional transformer encoders,” in ICASSP , 2020
2020
Closest in time.
W. Wang, Q. Tang, and K. Livescu, “Unsupervised pre-training of bidirectional speech encoders via masked reconstruction,” in ICASSP , 2020
2020
Closest in time.
2020
Closest in time.
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D. S. Park, W. Chan, Y. Zhang, C. Chiu, B. Zoph, E. D. Cubuk, and Q. V. Le, “Specaugment: A simple data augmentation method for automatic speech recognition,” in Interspeech , 2019
2019
Cited alongside, same era.
S. Karita, X. Wang, S. Watanabe, T. Yoshimura, W. Zhang, N. Chen, T. Hayashi, T. Hori, H. Inaguma, Z. Jiang, M. Someki, N. E. Y. Soplin, and R. Yamamoto, “A comparative study on transformer vs RNN in speech applications,” in ASRU , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
M. A. D. Gangi, R. Cattoni, L. Bentivogli, M. Negri, and M. Turchi, “Must-c: a multilingual speech translation corpus,” in NAACL-HLT , 2019
2019
Cited alongside, same era.
M. Neumann and N. T. Vu, “Improving speech emotion recognition with unsupervised representation learning on unlabeled speech,” in ICASSP , 2019
2019
Cited alongside, same era.
M. Ravanelli, T. Parcollet, and Y. Bengio, “The pytorch-kaldi speech recognition toolkit,” in ICASSP , 2019
2019
Cited alongside, same era.
A. Baevski, S. Schneider, and M. Auli, “vq-wav2vec: Self-supervised learning of discrete speech representations,” in ICLR , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
J. Kahn, M. Rivière, W. Zheng, E. Kharitonov, Q. Xu, P. Mazaré, J. Karadayi, V. Liptchinsky, R. Collobert, C. Fuegen, T. Likhomanenko, G. Synnaeve, A. Joulin, A. Mohamed, and E. Dupoux, “Libri-light: A benchmark for ASR with limited or no supervision,” in ICASSP , 2020
2020
Closest in time.
S. Ling, Y. Liu, J. Salazar, and K. Kirchhoff, “Deep contextualized acoustic representations for semi-supervised speech recognition,” in ICASSP , 2020
2020
Closest in time.
K. He, H. Fan, Y. Wu, S. Xie, and R. B. Girshick, “Momentum contrast for unsupervised visual representation learning,” in CVPR , 2020
2020
Closest in time.