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.- Anthology ID:
- 2024.iwslt-1.31
- Volume:
- Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand (in-person and online)
- Editors:
- Elizabeth Salesky, Marcello Federico, Marine Carpuat
- Venue:
- IWSLT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 265–273
- Language:
- URL:
- https://aclanthology.org/2024.iwslt-1.31
- DOI:
- Cite (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.
- Cite (Informal):
- Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation (Moslem, IWSLT 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.iwslt-1.31.pdf