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Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance.
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J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in
2017
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T. Hori, S. Watanabe, Y. Zhang, and W. Chan, “Advances in joint ctc-attention based end-to-end speech recognition with a deep cnn encoder and rnn-lm,” in
2017
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2018
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2018
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A. Renduchintala, S. Ding, M. Wiesner, and S. Watanabe, “Multi-modal data augmentation for end-to-end asr,” in
2018
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S. Watanabe, T. Hori, S. Karita, T. Hayashi, J. Nishitoba, Y. Unno, N. Enrique Yalta Soplin, J. Heymann, M. Wiesner, N. Chen, A. Renduchintala, and T. Ochiai, “Espnet: End-to-end speech processing toolkit,” in
2018
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T. Hori, R. Astudillo, T. Hayashi, Y. Zhang, S. Watanabe, and J. L. Roux, “Cycle-consistency training for end-to-end speech recognition,” in
2019
Closest in time.
A. Liu, H.-y. Lee, and L.-s. Lee, “Adversarial training of end-to-end speech recognition using a criticizing language model,” in
2019
Closest in time.