@inproceedings{e-ortega-etal-2025-quespa,
    title = "{QUESPA} Submission for the {IWSLT} 2025 Dialectal and Low-resource Speech Translation Task",
    author = "E. Ortega, John  and
      Joel Zevallos, Rodolfo  and
      Chen, William  and
      Abdulmumin, Idris",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Anastasopoulos, Antonis",
    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.iwslt-1.25/",
    pages = "260--268",
    ISBN = "979-8-89176-272-5",
    abstract = "This article describes the QUESPA team speech translation (ST) submissions for the Quechua to Spanish (QUE-SPA) track featured in the Evaluation Campaign of IWSLT 2025: dialectal and low-resource speech translation. This year, there is one main submission type supported in the campaign: unconstrained. This is our third year submitting our ST systems to the IWSLT shared task and we feel that we have achieved novel performance, surpassing last year{'}s submission. This year we submit three total unconstrained-only systems of which our best (contrastive 2) system uses last year{'}s best performing pre-trained language (PLM) model for ST (without cascading) and the inclusion of additional Quechua{--}Collao speech transcriptions found online. Fine-tuning of Microsoft{'}s SpeechT5 model in a ST setting along with the addition of new data and a data augmentation technique allowed us to achieve 26.7 BLEU. In this article, we present the three submissions along with a detailed description of the updated machine translation system where a comparison is done between synthetic, unconstrained, and other data for fine-tuning."
}