SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
Submission Type: Regular Long Paper
Submission Track: Machine Translation
Submission Track 2: Speech and Multimodality
Keywords: Speech Translation, Data Augmentation
TL;DR: We improve Speech Translation models by utilizing synthetic data that we created by re-segmenting the training data of ST corpora.
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.
Submission Number: 519
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