Self-Training for End-to-End Speech Translation

One of the main challenges for end-to-end speech translation is data scarcity.We leverage pseudo-labels generated from unlabeled audio by a cascade and an end-to-end speech translation model.This provides 8.3 and 5.7 BLEU gains over a strong semi-supervised baseline on the MuST-C English-French and English-German datasets, reaching state-of-the art performance.The effect of the quality of the pseudo-labels is investigated.Our approach is shown to be more effective than simply pre-training the encoder on the speech recognition task.Finally, we demonstrate the effectiveness of self-training by directly generating pseudo-labels with an end-to-end model instead of a cascade model.