论文标题
Segaugment：最大化语音翻译数据的实用性，以基于细分的增强
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
论文作者
论文摘要
缺乏可用的数据资源，阻碍了端到端的语音翻译。尽管其中大多数基于文档，但仍有一个句子级版本，但是它是单个且静态的，可能会阻碍数据的有用性。我们提出了一种新的数据增强策略Segaugment，以通过生成数据集的多个替代句子级版本来解决此问题。我们的方法利用音频分割系统，该系统通过不同的长度约束来重新分析每个文档的语音，然后我们通过对齐方法获得目标文本。实验表明，在必须使用的八个语言对中，平均增加了2.5个BLEU点，对于METTEX中的低资源场景，最高可达5个BLEU。此外，当与强大的系统结合使用时，Segaugment在必C中建立了新的最先进的结果。最后，我们表明所提出的方法还可以成功增强句子级数据集，并使语音翻译模型能够在推理时缩小手册和自动分割之间的差距。
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.
