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arXiv · 2004.14228

Meta-Transfer Learning for Code-Switched Speech Recognition

Genta Indra Winata·Samuel Cahyawijaya·Zhaojiang Lin·Zihan Liu·Peng Xu·Pascale Fung
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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.

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Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin, Zihan Liu, Peng Xu, Pascale Fung. 2020-04-29. Meta-Transfer Learning for Code-Switched Speech Recognition. https://arxiv.org/abs/2004.14228

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