Mai Ho'omāuna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian

Kaavya Chaparala, Guido Zarrella, Bruce Torres Fischer, Larry Kimura, Oiwi Parker Jones
4/3/2024
cs.CLcs.LGcs.SD

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Abstract

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.

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Cite this paper

@article{chaparala2024hoomuna,
  title  = {Mai Ho'omāuna 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},
  year   = {2024},
  eprint = {2404.03073},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.03073v1}
}

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