  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.
