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- W4287807005 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." @default.
- W4287807005 created "2022-07-26" @default.
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- W4287807005 date "2020-04-29" @default.
- W4287807005 modified "2023-10-17" @default.
- W4287807005 title "Meta-Transfer Learning for Code-Switched Speech Recognition" @default.
- W4287807005 doi "https://doi.org/10.48550/arxiv.2004.14228" @default.
- W4287807005 hasPublicationYear "2020" @default.
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