Data scarcity is one of the main issues with the end-to-end approach for
Speech Translation, as compared to the cascaded one. Although most data
resources for Speech Translation are originally document-level, they offer a
sentence-level view, which can be directly used during training. But this
sentence-level view is single and static, potentially limiting the utility of
the data. Our proposed data augmentation method SegAugment challenges this idea
and aims to increase data availability by providing multiple alternative
sentence-level views of a dataset. Our method heavily relies on an Audio
Segmentation system to re-segment the speech of each document, after which we
obtain the target text with alignment methods. The Audio Segmentation system
can be parameterized with different length constraints, thus giving us access
to multiple and diverse sentence-level views for each document. Experiments in
MuST-C show consistent gains across 8 language pairs, with an average increase
of 2.2 BLEU points, and up to 4.7 BLEU for lower-resource scenarios in mTEDx.
Additionally, we find that SegAugment is also applicable to purely
sentence-level data, as in CoVoST, and that it enables Speech Translation
models to completely close the gap between the gold and automatic segmentation
at inference time.

提出了一种名为 SegAugment 的数据增强方法，通过音频分割系统重新分割每个文档的语音，以获取多个目标文本，并得到多个和不同的句子级视图，实现了语音翻译中的数据增强，平均 BLEU 分数增加了 2.2 个点，并且在低资源场景下增加了 4.7 个 BLEU 点。