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Attention-based models have made tremendous progress on end-to-end automatic speech recognition(ASR) recently.
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2019
C. Shan, C. Weng, G. Wang, D. Su, M. Luo, D. Yu, and L. Xie, “Component fusion: Learning replaceable language model component for end-to-end speech recognition system,” in
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2019
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A. Mohamed, D. Okhonko, and L. Zettlemoyer, “Transformers with convolutional context for asr,”
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T. N. Sainath, Y. He, B. Li, A. Narayanan, R. Pang, A. Bruguier, S.-y. Chang, W. Li, R. Alvarez, Z. Chen
2020
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Cited alongside, same era.
S. Li, D. Raj, X. Lu, P. Shen, T. Kawahara, and H. Kawai, “Improving transformer-based speech recognition systems with compressed structure and speech attributes augmentation,”
2019
Cited alongside, same era.
T. Nakatani, “Improving transformer-based end-to-end speech recognition with connectionist temporal classification and language model integration,” 2019
2019
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2019
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L. Zhou, J. Zhang, and C. Zong, “Synchronous bidirectional neural machine translation,”
2019
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B. Li, S.-y. Chang, T. N. Sainath, R. Pang, Y. He, T. Strohman, and Y. Wu, “Towards fast and accurate streaming end-to-end asr,” in
2020
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H. Miao, G. Cheng, C. Gao, P. Zhang, and Y. Yan, “Transformer-based online ctc/attention end-to-end speech recognition architecture,” in
2020
Closest in time.