  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.
