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Attention-based beamformers have recently been shown to be effective for multi-channel speech recognition.
J. Capon, “High-resolution frequency-wavenumber spectrum analysis,”
1969
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,”
2012
Earlier work this paper cites.
A. Graves, “Sequence transduction with recurrent neural networks,”
2012
Earlier work this paper cites.
T. Hori, Z. Chen, H. Erdogan, J. R. Hershey, J. Le Roux, V. Mitra, and S. Watanabe, “The merl/sri system for the 3rd chime challenge using beamforming, robust feature extraction, and advanced speech recognition,” in
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
J. Heymann, L. Drude, and R. Haeb-Umbach, “Neural network based spectral mask estimation for acoustic beamforming,” in
2016
Earlier work this paper cites.
B. Li, T. N. Sainath, R. J. Weiss, K. W. Wilson, and M. Bacchiani, “Neural network adaptive beamforming for robust multichannel speech recognition,” in
2016
Earlier work this paper cites.
J. Li, A. R. Mohammad, G. Zweig, and Y. Gong, “Exploring multidimensional lstms for large vocabulary asr,” in
2016
Cited alongside, same era.
T. Ochiai, S. Watanabe, T. Hori, J. R. Hershey, and X. Xiao, “Unified architecture for multichannel end-to-end speech recognition with neural beamforming,”
2017
Cited alongside, same era.
T. N. Sainath, R. J. Weiss, K. W. Wilson, B. Li, A. Narayanan, E. Variani, M. Bacchiani, I. Shafran, A. Senior, K. Chin, A. Misra, and C. Kim, “Multichannel signal processing with deep neural networks for automatic speech recognition,”
2017
Cited alongside, same era.
B. Li, T. Sainath, A. Narayanan, J. Caroselli, M. Bacchiani, A. Misra, I. Shafran, H. Sak, G. Pundak, K. Chin, K. C. Sim, R. J. Weiss, K. Wilson, E. Variani, C. Kim, O. Siohan, M. Weintraub, E. McDermott, R. Rose, and M. Shannon, “Acoustic modeling for google home,” 2017
2017
Cited alongside, same era.
L. Dong, S. Xu, and B. Xu, “Speech-transformer: A no-recurrence sequence-to-sequence model for speech recognition,” in
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Braun, D. Neil, J. Anumula, E. Ceolini, and S. Liu, “Attention-driven multi-sensor selection,” in
2019
Later among the works it cites.
B. Yang, L. Wang, D. F. Wong, L. S. Chao, and Z. Tu, “Convolutional self-attention networks.” Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4040–4045. [Online]. Available: https://www.aclweb.org/anthology/N19-1407
2019
Later among the works it cites.
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2017
Cited alongside, same era.
S. Braun, D. Neil, J. Anumula, E. Ceolini, and S.-C. Liu, “Multi-channel attention for end-to-end speech recognition,” in
2018
Cited alongside, same era.
A. W. Yu, D. Dohan, T. Luong, R. Zhao, K. Chen, and Q. Le, “Qanet: Combining local convolution with global self-attention for reading comprehension,” 2018. [Online]. Available: https://openreview.net/pdf?id=B14TlG-RW
2018
Cited alongside, same era.
2019
Later among the works it cites.
W. He, L. Lu, B. Zhang, J. Mahadeokar, K. Kalgaonkar, and C. Fuegen, “Spatial attention for far-field speech recognition with deep beamforming neural networks,” in
2020
Later among the works it cites.
A. Gulati, C.-C. Chiu, J. Qin, J. Yu, N. Parmar, R. Pang, S. Wang, W. Han, Y. Wu, Y. Zhang, and Z. Zhang, Eds.,
2020
Later among the works it cites.