Meta-Transfer Learning for Code-Switched Speech Recognition

• URL: http://arxiv.org/abs/2004.14228v1
• Date: Wed, 29 Apr 2020 14:27:19 GMT
• Title: Meta-Transfer Learning for Code-Switched Speech Recognition
• Authors: Genta Indra Winata, Samuel Cahyawijaya, Zhaojiang Lin, Zihan Liu, Peng Xu, Pascale Fung
• Abstract summary: We propose a new learning method, meta-transfer learning, to transfer learn on a code-switched speech recognition system in a low-resource setting. 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.
• Score: 72.84247387728999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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