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Learning Noise Invariant Features Through Transfer Learning For Robust End-To-End Speech Recognition

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IEEE ICASSP 2020 Virtual Conference May 2020

End-to-end models yield impressive speech recognition results on clean datasets while having inferior performance on noisy datasets. To address this, we propose transfer learning from a clean dataset (WSJ) to a noisy dataset (CHiME-4) for connectionist te

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Learning Noise Invariant Features Through Transfer Learning For Robust End-To-End Speech Recognition

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End-to-end models yield impressive speech recognition results on clean datasets while having inferior performance on noisy datasets. To address this, we propose transfer learning from a clean dataset (WSJ) to a noisy dataset (CHiME-4) for connectionist te