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.

改善低资源语言夏威夷语的自动语音识别（ASR）的挑战，通过将大量独立文本数据整合到 Whisper 基础模型中，我们采用约 1.5M 字的夏威夷文本数据训练了外部语言模型（LM）。然后，我们使用该语言模型对 Whisper 进行评分，并计算标记的夏威夷数据测试集的词错误率（WER）。实验结果显示，在用夏威夷语言模型重新评分 ASR 输出时，WER 有小幅但显著的改善。结果支持在开发代表性语言的 ASR 系统时利用所有可用数据。

麦霍马乌纳伊卡艾：语言模型在夏威夷语自动语音识别中的改进

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

In contrast to the older writing system of the 19th century, modern Hawaiian
orthography employs characters for long vowels and glottal stops. These extra
characters account for about one-third of the phonemes in Hawaiian, so
including them makes a big difference to reading comprehension and
pronunciation. However, transliterating between older and newer texts is a
laborious task when performed manually. We introduce two related methods to
help solve this transliteration problem automatically, given that there were
not enough data to train an end-to-end deep learning model. One method is
implemented, end-to-end, using finite state transducers (FSTs). The other is a
hybrid deep learning approach which approximately composes an FST with a
recurrent neural network (RNN). We find that the hybrid approach outperforms
the end-to-end FST by partitioning the original problem into one part that can
be modelled by hand, using an FST, and into another part, which is easily
solved by an RNN trained on the available data.

本研究介绍了两种自动解决夏威夷文老字体和新字体之间转译问题的方法，分别采用有限状态转录器和深度学习方法。研究发现，一个混合模型可以将问题划分为由 FST 模型处理的部分和由 RNN 模型训练解决的部分，最终超越了使用有限状态转录器的端到端模型的表现。