@inproceedings{le-ferrand-prudhommeaux-2024-automatic,
    title = "Automatic Transcription of Grammaticality Judgements for Language Documentation",
    author = "Le Ferrand, {\'E}ric  and
      Prud{'}hommeaux, Emily",
    editor = "Moeller, Sarah  and
      Agyapong, Godfred  and
      Arppe, Antti  and
      Chaudhary, Aditi  and
      Rijhwani, Shruti  and
      Cox, Christopher  and
      Henke, Ryan  and
      Palmer, Alexis  and
      Rosenblum, Daisy  and
      Schwartz, Lane",
    booktitle = "Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.computel-1.6/",
    pages = "33--38",
    abstract = "Descriptive linguistics is a sub-field of linguistics that involves the collection and annotationof language resources to describe linguistic phenomena. The transcription of these resources is often described as a tedious task, and Automatic Speech Recognition (ASR) has frequently been employed to support this process. However, the typical research approach to ASR in documentary linguistics often only captures a subset of the field`s diverse reality. In this paper, we focus specifically on one type of data known as grammaticality judgment elicitation in the context of documenting Kr{\'e}y{\`o}l Gwadloup{\'e}yen. We show that only a few minutes of speech is enough to fine-tune a model originally trained in French to transcribe segments in Kr{\'e}yol."
}