@inproceedings{suchardt-etal-2025-towards,
    title = "Towards Language-Agnostic {STIPA}: Universal Phonetic Transcription to Support Language Documentation at Scale",
    author = "Suchardt, Jacob Lee  and
      El-Shazli, Hana  and
      Cassotti, Pierluigi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.1600/",
    doi = "10.18653/v1/2025.emnlp-main.1600",
    pages = "31411--31427",
    ISBN = "979-8-89176-332-6",
    abstract = "This paper explores the use of existing state-of-the-art speech recognition models (ASR) for the task of generating narrow phonetic transcriptions using the International Phonetic Alphabet (STIPA). Unlike conventional ASR systems focused on orthographic output for high-resource languages, STIPA can be used as a language-agnostic interface valuable for documenting under-resourced and unwritten languages. We introduce a new dataset for South Levantine Arabic and present the first large-scale evaluation of STIPA models across 51 language families. Additionally, we provide a use case on Sanna, a severely endangered language. Our findings show that fine-tuned ASR models can produce accurate IPA transcriptions with limited supervision, significantly reducing phonetic error rates even in extremely low-resource settings. The results highlight the potential of STIPA for scalable language documentation."
}