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Meta Learning For End-To-End Low-Resource Speech Recognition

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IEEE ICASSP 2020 Virtual Conference May 2020

In this paper, we proposed to apply meta learning approach for low-resource automatic speech recognition (ASR). We formulated ASR for different languages as different tasks, and meta-learned the initialization parameters from many pretraining languages to

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Meta Learning For End-To-End Low-Resource Speech Recognition

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In this paper, we proposed to apply meta learning approach for low-resource automatic speech recognition (ASR). We formulated ASR for different languages as different tasks, and meta-learned the initialization parameters from many pretraining languages to