In this paper we address the challenge of improving Automatic Speech Recognition (ASR) for a low-resource language, Hawaiian, by incorporating large amounts of independent text data into an ASR foundation model, Whisper. To do this, we train an external language model (LM) on ~1.5M words of Hawaiian text. We then use the LM to rescore Whisper and compute word error rates (WERs) on a manually curated test set of labeled Hawaiian data. As a baseline, we use Whisper without an external LM. Experimental results reveal a small but significant improvement in WER when ASR outputs are rescored with a Hawaiian LM. The results support leveraging all available data in the development of ASR systems for underrepresented languages.
@article{arxiv.2404.03073,
title = {Mai Ho'om\=auna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian},
author = {Kaavya Chaparala and Guido Zarrella and Bruce Torres Fischer and Larry Kimura and Oiwi Parker Jones},
journal= {arXiv preprint arXiv:2404.03073},
year = {2024}
}