Large-Scale Self- and Semi-Supervised Learning for Speech Translation

• URL: http://arxiv.org/abs/2104.06678v1
• Date: Wed, 14 Apr 2021 07:44:52 GMT
• Title: Large-Scale Self- and Semi-Supervised Learning for Speech Translation
• Authors: Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau
• Abstract summary: 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.
• Score: 48.06478781295623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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