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Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation

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arxiv 2406.17363 v2 pith:5IZLIVQU submitted 2024-06-25 cs.CL cs.SDeess.AS

Yasmin Moslem This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords dataspeechtranslationaudioaugmentationend-to-endsyntheticback-translation
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. It's Not a Walk in the Park! Challenges of Idiom Translation in Speech-to-text Systems

    cs.CL 2025-06 conditional novelty 6.0 of 10

    End-to-end speech translation systems translate idioms worse than text-based systems, frequently producing literal or incorrect outputs, across German and Russian to English.

  2. GMU Systems for the IWSLT 2025 Low-Resource Speech Translation Shared Task

    cs.CL 2025-05 conditional novelty 4.0 of 10

    Fine-tuning SeamlessM4T-v2 directly for end-to-end speech translation is competitive, and ASR-encoder initialization adds about 1 to 5 BLEU for languages unseen by the base model.

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