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
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.