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Adaption of end-to-end speech recognition systems to new tasks is known to be challenging.
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A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing , 2013, pp. 6645–6649
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V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: An asr corpus based on public domain audio books,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2015, pp. 5206–5210
2015
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W. Chan, N. Jaitly, Q. V. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in ICASSP , 2016
2016
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K. Rao, H. Sak, and R. Prabhavalkar, “Exploring architectures, data and units for streaming end-to-end speech recognition with rnn-transducer,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , 2017, pp. 193–199
2017
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A. Kannan, Y. Wu, P. Nguyen, T. N. Sainath, Z. Chen, and R. Prabhavalkar, “An analysis of incorporating an external language model into a sequence-to-sequence model,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2018, pp. 1–5828
P. J. Ortiz Suárez, B. Sagot, and L. Romary, “Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures,” in 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7) , P. Bański, A. Barbaresi, H. Biber, E. Breiteneder, S. Clematide, M. Kupietz, H. Lüngen, and C. Iliadi, Eds. Cardiff, United Kingdom: Leibniz-Institut für Deutsche Sprache, Jul. 2019. [Online]. Available: https://hal.inria.fr/hal-02148693
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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 Proc. Interspeech 2019 , 2019, pp. 2613–2617. [Online]. Available: http://dx.doi.org/10.21437/Interspeech.2019-2680
2019
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J. Li, R. Zhao, Z. Meng, Y. Liu, W. Wei, S. Parthasarathy, V. Mazalov, Z. Wang, L. He, S. Zhao, and Y. Gong, “Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability,” in Proc. Interspeech 2020 , 2020, pp. 3590–3594. [Online]. Available: http://dx.doi.org/10.21437/Interspeech.2020-3016
2020
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2018
Cited alongside, same era.
F. Hernandez, V. Nguyen, S. Ghannay, N. Tomashenko, and Y. Estève, “Ted-lium 3: Twice as much data and corpus repartition for experiments on speaker adaptation,” in Speech and Computer , A. Karpov, O. Jokisch, and R. Potapova, Eds. Cham: Springer International Publishing, 2018, pp. 198–208
2018
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Y. He, T. N. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y. Wu, R. Pang, Q. Liang, D. Bhatia, Y. Shangguan, B. Li, G. Pundak, K. C. Sim, T. Bagby, S. Chang, K. Rao, and A. Gruenstein, “Streaming end-to-end speech recognition for mobile devices,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 6381–6385
2019
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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 ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 5361–5635
2019
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Later among the works it cites.
M. Ghodsi, X. Liu, J. Apfel, R. Cabrera, and E. Weinstein, “Rnn-transducer with stateless prediction network,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 7049–7053
2020
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R. Ardila, M. Branson, K. Davis, M. Kohler, J. Meyer, M. Henretty, R. Morais, L. Saunders, F. Tyers, and G. Weber, “Common voice: A massively-multilingual speech corpus,” in Proceedings of the 12th Language Resources and Evaluation Conference . Marseille, France: European Language Resources Association, May 2020, pp. 4218–4222. [Online]. Available: https://www.aclweb.org/anthology/2020.lrec-1.520
2020
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E. Bastianelli, A. Vanzo, P. Swietojanski, and V. Rieser, “SLURP: A spoken language understanding resource package,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Online: Association for Computational Linguistics, Nov. 2020, pp. 7252–7262. [Online]. Available: https://www.aclweb.org/anthology/2020.emnlp-main.588
2020
Later among the works it cites.
Z. Meng, S. Parthasarathy, E. Sun, Y. Gaur, N. Kanda, L. Lu, X. Chen, R. Zhao, J. Li, and Y. Gong, “Internal language model estimation for domain-adaptive end-to-end speech recognition,” in Proceedings of IEEE SLT , 2021
2021
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