@inproceedings{booshanam-etal-2025-spoken,
    title = "Spoken Document Retrieval for an Unwritten Language: A Case Study on Gormati",
    author = "Booshanam, Sanjay  and
      Chen, Kelly  and
      Klejch, Ondrej  and
      Reitmaier, Thomas  and
      Raju, Dani Kalarikalayil  and
      Wallington, Electra  and
      Markl, Nina  and
      Pearson, Jennifer  and
      Jones, Matt  and
      Robinson, Simon  and
      Bell, Peter",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1224/",
    doi = "10.18653/v1/2025.findings-emnlp.1224",
    pages = "22497--22509",
    ISBN = "979-8-89176-335-7",
    abstract = "Speakers of unwritten languages have the potential to benefit from speech-based automatic information retrieval systems. This paper proposes a speech embedding technique that facilitates such a system that we can be used in a zero-shot manner on the target language. After conducting development experiments on several written Indic languages, we evaluate our method on a corpus of Gormati {--} an unwritten language {--} that was previously collected in partnership with an agrarian Banjara community in Maharashtra State, India, specifically for the purposes of information retrieval. Our system achieves a Top 5 retrieval rate of 87.9{\%} on this data, giving the hope that it may be useable by unwritten language speakers worldwide."
}