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        <title>SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations</title>
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        <namePart type="given">Ioannis</namePart>
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        <namePart type="given">José</namePart>
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    <name type="personal">
        <namePart type="given">Marta</namePart>
        <namePart type="family">Costa-jussà</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.574</identifier>
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%0 Conference Proceedings
%T SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
%A Tsiamas, Ioannis
%A Fonollosa, José
%A Costa-jussà, Marta
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F tsiamas-etal-2023-segaugment
%X 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.
%R 10.18653/v1/2023.findings-emnlp.574
%U https://aclanthology.org/2023.findings-emnlp.574/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.574
%P 8569-8588

Markdown (Informal)

[SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations](https://aclanthology.org/2023.findings-emnlp.574/) (Tsiamas et al., Findings 2023)

• SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations (Tsiamas et al., Findings 2023)

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.