@inproceedings{le-ferrand-henri-2026-child,
    title = "Child Support: Leveraging Lexifiers Resources to Support Creoles {ASR}",
    author = "Le Ferrand, {\'E}ric  and
      Henri, Fabiola",
    editor = "Agyapong, Godfred  and
      Moeller, Sarah  and
      Arppe, Antti  and
      Marashian, Ali  and
      Rosenblum, Daisy",
    booktitle = "Proceedings of the Ninth Workshop on the Use of Computational Methods in the Study of Endangered Languages ({C}omput{EL}-9)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.computel-1.12/",
    pages = "111--117",
    ISBN = "979-8-89176-422-4",
    abstract = "Creole languages emerged from colonial contact and the slave trade. Although they inheritthe bulk of their vocabulary from a ``lexifier{''}language, they remain classic low-resourcelanguages, presenting significant challengesfor speech technology. This paper exploreshow the abundant resources of a lexifier canbe leveraged for Creole-specific tools, focusing on Automatic Speech Recognition (ASR).Specifically, we use an artificial dataset generated a French-trained Text-to-Speech (TTS)model and French datasets to pre-finetune ASRmodels for two French-based Creoles. Ourresults demonstrate that a two-stage trainingsetup where models are first trained on artificial datasets leads to substantial performanceboost for transcribing Creole languages. Additionally, this approach serves as a viable firststep for ASR development in zero-resource scenarios."
}