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.
@article{arxiv.2006.02490,
title = {Self-Training for End-to-End Speech Translation},
author = {Juan Pino and Qiantong Xu and Xutai Ma and Mohammad Javad Dousti and Yun Tang},
journal= {arXiv preprint arXiv:2006.02490},
year = {2020}
}