@inproceedings{winata-etal-2020-meta,
title = "Meta-Transfer Learning for Code-Switched Speech Recognition",
author = "Winata, Genta Indra and
Cahyawijaya, Samuel and
Lin, Zhaojiang and
Liu, Zihan and
Xu, Peng and
Fung, Pascale",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.348",
doi = "10.18653/v1/2020.acl-main.348",
pages = "3770--3776",
abstract = "An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains difficult due to the availability of limited resources and the expense and significant effort required to collect mixed-language data. We therefore propose a new learning method, meta-transfer learning, to transfer learn on a code-switched speech recognition system in a low-resource setting by judiciously extracting information from high-resource monolingual datasets. Our model learns to recognize individual languages, and transfer them so as to better recognize mixed-language speech by conditioning the optimization on the code-switching data. Based on experimental results, our model outperforms existing baselines on speech recognition and language modeling tasks, and is faster to converge.",
}
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<abstract>An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains difficult due to the availability of limited resources and the expense and significant effort required to collect mixed-language data. We therefore propose a new learning method, meta-transfer learning, to transfer learn on a code-switched speech recognition system in a low-resource setting by judiciously extracting information from high-resource monolingual datasets. Our model learns to recognize individual languages, and transfer them so as to better recognize mixed-language speech by conditioning the optimization on the code-switching data. Based on experimental results, our model outperforms existing baselines on speech recognition and language modeling tasks, and is faster to converge.</abstract>
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%0 Conference Proceedings
%T Meta-Transfer Learning for Code-Switched Speech Recognition
%A Winata, Genta Indra
%A Cahyawijaya, Samuel
%A Lin, Zhaojiang
%A Liu, Zihan
%A Xu, Peng
%A Fung, Pascale
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F winata-etal-2020-meta
%X An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains difficult due to the availability of limited resources and the expense and significant effort required to collect mixed-language data. We therefore propose a new learning method, meta-transfer learning, to transfer learn on a code-switched speech recognition system in a low-resource setting by judiciously extracting information from high-resource monolingual datasets. Our model learns to recognize individual languages, and transfer them so as to better recognize mixed-language speech by conditioning the optimization on the code-switching data. Based on experimental results, our model outperforms existing baselines on speech recognition and language modeling tasks, and is faster to converge.
%R 10.18653/v1/2020.acl-main.348
%U https://aclanthology.org/2020.acl-main.348
%U https://doi.org/10.18653/v1/2020.acl-main.348
%P 3770-3776
Markdown (Informal)
[Meta-Transfer Learning for Code-Switched Speech Recognition](https://aclanthology.org/2020.acl-main.348) (Winata et al., ACL 2020)
ACL
- Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin, Zihan Liu, Peng Xu, and Pascale Fung. 2020. Meta-Transfer Learning for Code-Switched Speech Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3770–3776, Online. Association for Computational Linguistics.