@inproceedings{havard-etal-2025-speech,
    title = "Speech Technologies with Fieldwork Recordings: the Case of {H}aitian {C}reole",
    author = "Havard, William N.  and
      Govain, Renauld  and
      Lecouteux, Benjamin  and
      Schang, Emmanuel",
    editor = "Lachler, Jordan  and
      Agyapong, Godfred  and
      Arppe, Antti  and
      Moeller, Sarah  and
      Chaudhary, Aditi  and
      Rijhwani, Shruti  and
      Rosenblum, Daisy",
    booktitle = "Proceedings of the Eight Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2025",
    address = "Honolulu, Hawaii, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.computel-main.5/",
    pages = "40--46",
    ISBN = "None",
    abstract = "We use 40-year-old digitalised tape-recorded fieldwork data in Haitian Creole to train a native self-supervised learning (SSL) model of speech representation (WAV2VEC2). We also use a continued pre-training approach on pre-trained SSL models of two foreign languages the lexifier language {--} French {--} and an unrelated language {--} English. We compare the performances of these three SSL models, and of two other foreign SSL models directly finetuned, on an ASR task, where all five models are fine-tuned on transcribed fieldwork recordings in Haitian Creole. Our results show the best-performing model is the one trained using a continued pre-training approach on the lexifier language, followed by the native model. We conclude that the {\textquoteleft}mobilising the archive'-approach advocated by (Bird, 2020) is a promising way forward to design speech technologies for new languages."
}