In this paper, we improve speech translation (ST) through effectively leveraging large quantities of unlabeled speech and text data in different and complementary ways. We explore both pretraining and self-training by using the large Libri-Light speech audio corpus and language modeling with CommonCrawl. Our experiments improve over the previous state of the art by 2.6 BLEU on average on all four considered CoVoST 2 language pairs via a simple recipe of combining wav2vec 2.0 pretraining, a single iteration of self-training and decoding with a language model. Different to existing work, our approach does not leverage any other supervision than ST data. Code and models will be publicly released.
@article{arxiv.2104.06678,
title = {Large-Scale Self- and Semi-Supervised Learning for Speech Translation},
author = {Changhan Wang and Anne Wu and Juan Pino and Alexei Baevski and Michael Auli and Alexis Conneau},
journal= {arXiv preprint arXiv:2104.06678},
year = {2021}
}