Abstract
For the 2023 IWSLT (Agarwal et al., 2023) Maltese Speech Translation Task, UM-DFKI jointly presents a cascade solution which achieves 0.6 BLEU. While this is the first time that a Maltese speech translation task has been released by IWSLT, this paper explores previous solutions for other speech translation tasks, focusing primarily on low-resource scenarios. Moreover, we present our method of fine-tuning XLS-R models for Maltese ASR using a collection of multi-lingual speech corpora as well as the fine-tuning of the mBART model for Maltese to English machine translation.
Cite
CITATION STYLE
Williams, A., Abela, K., Kumar, R., Bär, M., Billinghurst, H., De Marco, A., … Borg, C. (2023). UM-DFKI Maltese Speech Translation. In 20th International Conference on Spoken Language Translation, IWSLT 2023 - Proceedings of the Conference (pp. 433–441). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.iwslt-1.41
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