  End-to-end Speech Translation is hindered by a lack of available data
resources. While most of them are based on documents, a sentence-level version
is available, which is however single and static, potentially impeding the
usefulness of the data. We propose a new data augmentation strategy,
SegAugment, to address this issue by generating multiple alternative
sentence-level versions of a dataset. Our method utilizes an Audio Segmentation
system, which re-segments the speech of each document with different length
constraints, after which we obtain the target text via alignment methods.
Experiments demonstrate consistent gains across eight language pairs in MuST-C,
with an average increase of 2.5 BLEU points, and up to 5 BLEU for low-resource
scenarios in mTEDx. Furthermore, when combined with a strong system, SegAugment
establishes new state-of-the-art results in MuST-C. Finally, we show that the
proposed method can also successfully augment sentence-level datasets, and that
it enables Speech Translation models to close the gap between the manual and
automatic segmentation at inference time.
