Built on

J. B. Hampshire II and A. Waibel, “The meta-pi network: Building distributed knowledge representations for robust multisource pattern recognition,” IEEE Computer Architecture Letters , 1992
1992
Earlier work this paper cites.
D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio, “End-to-end attention-based large vocabulary speech recognition,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016
2016
Earlier work this paper cites.
W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 4960–4964
2016
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL) , 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
S.-A. Rebuffi, H. Bilen, and A. Vedaldi, “Learning multiple visual domains with residual adapters,” Advances in neural information processing systems , vol. 30, 2017
2017
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
E. A. Platanios, M. Sachan, G. Neubig, and T. Mitchell, “Contextual parameter generation for universal neural machine translation,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , Brussels, Belgium, 2018
2018
Earlier work this paper cites.
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,” arXiv , 2019
2019
Earlier work this paper cites.
N.-Q. Pham, T.-S. Nguyen, J. Niehues, M. Müller, and A. Waibel, “Very Deep Self-Attention Networks for End-to-End Speech Recognition,” in Proc. Interspeech 2019 , 2019, pp. 66–70
2019
Earlier work this paper cites.
T.-S. Nguyen, S. Stueker, J. Niehues, and A. Waibel, “Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation,” arXiv preprint arXiv:1910.13296 , 2019
Original
2019
Earlier work this paper cites.
S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” arXiv preprint arXiv:1904.05862 , 2019
Original
2019
Earlier work this paper cites.
Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. Le, and R. Salakhutdinov, “Transformer-XL: Attentive language models beyond a fixed-length context,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019
2019
Earlier work this paper cites.

Similar

Then

Y. Tang, C. Tran, X. Li, P.-J. Chen, N. Goyal, V. Chaudhary, J. Gu, and A. Fan, “Multilingual translation with extensible multilingual pretraining and finetuning,” arXiv preprint arXiv:2008.00401 , 2020
Original
2020
Later among the works it cites.
Y. Wen, D. Tran, and J. Ba, “Batchensemble: an alternative approach to efficient ensemble and lifelong learning,” arXiv preprint , 2020
2020
Later among the works it cites.
C. Wang, Y. Wu, S. Liu, Z. Yang, and M. Zhou, “Bridging the gap between pre-training and fine-tuning for end-to-end speech translation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, 2020, pp. 9161–9168
2020
Later among the works it cites.
J. Iranzo-Sánchez, J. A. Silvestre-Cerdà, J. Jorge, N. Roselló, A. Giménez, A. Sanchis, J. Civera, and A. Juan, “Europarl-st: A multilingual corpus for speech translation of parliamentary debates,” in ICASSP , 2020
2020
Later among the works it cites.

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2022

Adaptive multilingual speech recognition with pretrained models

Pham, Ngoc-Quan, Waibel, Alex, Niehues, Jan

Understand

Multilingual speech recognition with supervised learning has achieved great results as reflected in recent research.

With the development of pretraining methods on audio and text data, it is imperative to transfer the knowledge from unsupervised multilingual models to facilitate recognition, especially in many languages with limited data.
Our work investigated the effectiveness of using two pretrained models for two modalities: wav2vec 2.0 for audio and MBART50 for text, together with the adaptive weight techniques to massively improve the recognition quality on the public datasets containing CommonVoice and Europarl.
Overall, we noticed an 44% improvement over purely supervised learning, and more importantly, each technique provides a different reinforcement in different languages.

Built on

J. B. Hampshire II and A. Waibel, “The meta-pi network: Building distributed knowledge representations for robust multisource pattern recognition,” IEEE Computer Architecture Letters , 1992
1992
Earlier work this paper cites.
D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio, “End-to-end attention-based large vocabulary speech recognition,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016
2016
Earlier work this paper cites.
W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 4960–4964
2016
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL) , 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
S.-A. Rebuffi, H. Bilen, and A. Vedaldi, “Learning multiple visual domains with residual adapters,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
E. A. Platanios, M. Sachan, G. Neubig, and T. Mitchell, “Contextual parameter generation for universal neural machine translation,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , Brussels, Belgium, 2018
2018
Earlier work this paper cites.
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,” arXiv , 2019
2019
Earlier work this paper cites.
N.-Q. Pham, T.-S. Nguyen, J. Niehues, M. Müller, and A. Waibel, “Very Deep Self-Attention Networks for End-to-End Speech Recognition,” in Proc. Interspeech 2019 , 2019, pp. 66–70
2019
Earlier work this paper cites.
T.-S. Nguyen, S. Stueker, J. Niehues, and A. Waibel, “Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation,” arXiv preprint arXiv:1910.13296 , 2019
Original
2019
Earlier work this paper cites.
S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” arXiv preprint arXiv:1904.05862 , 2019
Original
2019
Earlier work this paper cites.
Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. Le, and R. Salakhutdinov, “Transformer-XL: Attentive language models beyond a fixed-length context,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019
2019
Earlier work this paper cites.

Similar

Then

Y. Tang, C. Tran, X. Li, P.-J. Chen, N. Goyal, V. Chaudhary, J. Gu, and A. Fan, “Multilingual translation with extensible multilingual pretraining and finetuning,” arXiv preprint arXiv:2008.00401 , 2020
Original
2020
Later among the works it cites.
Y. Wen, D. Tran, and J. Ba, “Batchensemble: an alternative approach to efficient ensemble and lifelong learning,” arXiv preprint , 2020
2020
Later among the works it cites.
C. Wang, Y. Wu, S. Liu, Z. Yang, and M. Zhou, “Bridging the gap between pre-training and fine-tuning for end-to-end speech translation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, 2020, pp. 9161–9168
2020
Later among the works it cites.
J. Iranzo-Sánchez, J. A. Silvestre-Cerdà, J. Jorge, N. Roselló, A. Giménez, A. Sanchis, J. Civera, and A. Juan, “Europarl-st: A multilingual corpus for speech translation of parliamentary debates,” in ICASSP , 2020
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799

2019

Cited alongside, same era.

R. Ardila, M. Branson, K. Davis, M. Henretty, M. Kohler, J. Meyer, R. Morais, L. Saunders, F. M. Tyers, and G. Weber, “Common voice: A massively-multilingual speech corpus,” arXiv preprint arXiv:1912.06670 , 2019

Original

2019

Cited alongside, same era.

A. Gulati et al. , “Conformer: Convolution-augmented transformer for speech recognition,” arXiv preprint arXiv:2005.08100 , 2020

Original

2020

Cited alongside, same era.

V. Pratap, A. Sriram, P. Tomasello, A. Hannun, V. Liptchinsky, G. Synnaeve, and R. Collobert, “Massively multilingual asr: 50 languages, 1 model, 1 billion parameters,” arXiv preprint arXiv:2007.03001 , 2020

Original

2020

Cited alongside, same era.

N.-Q. Pham, T.-L. Ha, T.-N. Nguyen, T.-S. Nguyen, E. Salesky, S. Stüker, J. Niehues, and A. Waibel, “Relative Positional Encoding for Speech Recognition and Direct Translation,” in Proc. Interspeech 2020

2020

Cited alongside, same era.

A. Baevski, Y. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” Advances in Neural Information Processing Systems , vol. 33, pp. 12 449–12 460, 2020

2020

Cited alongside, same era.

X. Li, C. Wang, Y. Tang, C. Tran, Y. Tang, J. Pino, A. Baevski, A. Conneau, and M. Auli, “Multilingual speech translation with efficient finetuning of pretrained models,” arXiv preprint arXiv:2010.12829 , 2020

Original

2020

Cited alongside, same era.

Y. Liu, J. Gu, N. Goyal, X. Li, S. Edunov, M. Ghazvininejad, M. Lewis, and L. Zettlemoyer, “Multilingual denoising pre-training for neural machine translation,” Transactions of the Association for Computational Linguistics , vol. 8, pp. 726–742, 2020

2020

Cited alongside, same era.

V. Pratap, Q. Xu, A. Sriram, G. Synnaeve, and R. Collobert, “Mls: A large-scale multilingual dataset for speech research,” arXiv preprint arXiv:2012.03411 , 2020

Original

2020