  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.
