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arXiv · 2110.03560

Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0

Sameer Khurana·Antoine Laurent·James Glass
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Abstract

We propose a simple and effective cross-lingual transfer learning method to adapt monolingual wav2vec-2.0 models for Automatic Speech Recognition (ASR) in resource-scarce languages. We show that a monolingual wav2vec-2.0 is a good few-shot ASR learner in several languages. We improve its performance further via several iterations of Dropout Uncertainty-Driven Self-Training (DUST) by using a moderate-sized unlabeled speech dataset in the target language. A key finding of this work is that the adapted monolingual wav2vec-2.0 achieves similar performance as the topline multilingual XLSR model, which is trained on fifty-three languages, on the target language ASR task.

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Sameer Khurana, Antoine Laurent, James Glass. 2021-10-07. Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0. https://doi.org/10.1109/icassp43922.2022.9746276

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