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        <title>Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation</title>
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        <namePart type="given">Yasmin</namePart>
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            <title>Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)</title>
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        <name type="personal">
            <namePart type="given">Elizabeth</namePart>
            <namePart type="family">Salesky</namePart>
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        <name type="personal">
            <namePart type="given">Marcello</namePart>
            <namePart type="family">Federico</namePart>
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        <name type="personal">
            <namePart type="given">Marine</namePart>
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    <abstract>This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity.</abstract>
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%0 Conference Proceedings
%T Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation
%A Moslem, Yasmin
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Carpuat, Marine
%S Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand (in-person and online)
%F moslem-2024-leveraging
%X This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity.
%U https://aclanthology.org/2024.iwslt-1.31
%P 265-273

Markdown (Informal)

[Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation](https://aclanthology.org/2024.iwslt-1.31) (Moslem, IWSLT 2024)

• Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation (Moslem, IWSLT 2024)

ACL

• Yasmin Moslem. 2024. Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pages 265–273, Bangkok, Thailand (in-person and online). Association for Computational Linguistics.