Spoken Document Retrieval for an Unwritten Language: A Case Study on Gormati
Sanjay Booshanam, Kelly Chen, Ondrej Klejch, Thomas Reitmaier, Dani Kalarikalayil Raju, Electra Wallington, Nina Markl, Jennifer Pearson
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.
BibTeX
@inproceedings{emnlp2025_spokendocumentre,
title = {Spoken Document Retrieval for an Unwritten Language: A Case Study on Gormati},
author = {Sanjay Booshanam and Kelly Chen and Ondrej Klejch and Thomas Reitmaier and Dani Kalarikalayil Raju and Electra Wallington and Nina Markl and Jennifer Pearson and Matt Jones and Simon Robinson and Peter Bell},
booktitle = {EMNLP 2025},
year = {2025}
}