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

• URL: http://arxiv.org/abs/2110.03560v1
• Date: Thu, 7 Oct 2021 15:29:22 GMT
• Title: Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0
• Authors: Sameer Khurana, Antoine Laurent, James Glass
• Abstract summary: We show that a monolingual wav2vec-2.0 is a good few-shot ASR learner in several languages. A key finding of this work is that the adapted monolingual wav2vec-2.0 achieves similar performance as the topline multilingual XLSR model.
• Score: 7.378368959253632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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