Miftahul Jannat


2024

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UM IWSLT 2024 Low-Resource Speech Translation: Combining Maltese and North Levantine Arabic
Sara Nabhani | Aiden Williams | Miftahul Jannat | Kate Rebecca Belcher | Melanie Galea | Anna Taylor | Kurt Micallef | Claudia Borg
Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)

The IWSLT low-resource track encourages innovation in the field of speech translation, particularly in data-scarce conditions. This paper details our submission for the IWSLT 2024 low-resource track shared task for Maltese-English and North Levantine Arabic-English spoken language translation using an unconstrained pipeline approach. Using language models, we improve ASR performance by correcting the produced output. We present a 2 step approach for MT using data from external sources showing improvements over baseline systems. We also explore transliteration as a means to further augment MT data and exploit the cross-lingual similarities between Maltese and Arabic.