Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation
<|reference_start|>Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation: 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.<|reference_end|>
@article{moslem2024leveraging,
    title={Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech
  Translation},
    author={Yasmin Moslem},
    journal={arXiv preprint arXiv:2406.17363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17363},
    primaryClass={cs.CL cs.SD eess.AS}
}