SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations

Ioannis Tsiamas, José Fonollosa, Marta Costa-jussà

Abstract
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 obtains 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.
Anthology ID:
2023.findings-emnlp.574
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8569–8588
Language:
URL:
https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.574/
DOI:
10.18653/v1/2023.findings-emnlp.574
Bibkey:
Cite (ACL):
Ioannis Tsiamas, José Fonollosa, and Marta Costa-jussà. 2023. SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8569–8588, Singapore. Association for Computational Linguistics.
Cite (Informal):
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations (Tsiamas et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.574.pdf
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