Meta-Transfer Learning for Code-Switched Speech Recognition
Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin, Zihan Liu, Peng Xu, Pascale Fung
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.- Anthology ID:
- 2020.acl-main.348
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3770–3776
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.348
- DOI:
- 10.18653/v1/2020.acl-main.348
- Cite (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.
- Cite (Informal):
- Meta-Transfer Learning for Code-Switched Speech Recognition (Winata et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.348.pdf
- Code
- audioku/meta-transfer-learning