تقرير
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
| العنوان: | SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations |
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| المؤلفون: | Tsiamas, Ioannis, Fonollosa, José A. R., Costa-jussà, Marta R. |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | 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. Comment: EMNLP 2023 (Findings) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2212.09699 |
| رقم الأكسشن: | edsarx.2212.09699 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |