@inproceedings{mohammadamini-tahon-2026-lium,
    title = "{LIUM} Submission for {IWSLT} 2026 Low-resource Speech Translation Track",
    author = "Mohammadamini, Mohammad  and
      Tahon, Marie",
    editor = "Salesky, Elizabeth  and
      Anastasopoulos, Antonios  and
      Negri, Matteo  and
      Federico, Marcello",
    booktitle = "Proceedings of the 23rd International Conference on Spoken Language Translation ({IWSLT} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, USA (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2026-06/2026.iwslt-1.15/",
    doi = "10.18653/v1/2026.iwslt-1.15",
    pages = "124--131",
    ISBN = "979-8-89176-411-8",
    abstract = "This paper describes the LIUM submission to the IWSLT 2026 low-resource speech translation track. It proposes different data augmentation methods for low-resource speech-to-text translation, including two main pipelines: pseudo-labeling and speech synthesis. The goal is to generate parallel speech data in low-resource scenarios without relying on human-annotated speech translation data. Our submission focuses on Central Kurdish{--}English language pairs. The objective of this work is to explore the advantages and limitations of each data augmentation method. Our best results are obtained using the pseudo-labeling pipeline, achieving a BLEU score of 25.73 on the development set and 21.09 on the test set for Central Kurdish{--}English translation."
}