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.

翻译：端到端语音翻译受限于可用数据资源的匮乏。尽管现有数据多基于文档形式，但也存在基于句子的版本，然而这些版本单一且静态，可能限制数据的实用性。为此，我们提出一种新的数据增强策略SegAugment，通过生成数据集的多重替代句子级版本来解决该问题。该方法利用音频分割系统，以不同长度约束对每个文档的语音进行重新分割，随后通过对齐方法获取目标文本。实验表明，该方法在MuST-C语料库的八个语言对上持续提升性能，平均提升2.5个BLEU值，在mTEDx低资源场景下最高提升5个BLEU值。此外，SegAugment与强基线系统结合后，在MuST-C上创下新的最优结果。最终实验证明，该方法能有效增强句子级数据集，并帮助语音翻译模型在推理时缩短人工分割与自动分割之间的性能差距。