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Mai Ho'om=auna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian

arXiv 2404.03073 ↗First posted 3 Apr 2024cs.CL cs.LG cs.SDcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 3 Apr 2024.

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References · 18

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6 more
  1. 2019End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architecturescited by 120
  2. 2020Iterative Pseudo-Labeling for Speech Recognitioncited by 81
  3. 2020SlimIPL: Language-Model-Free Iterative Pseudo-Labelingcited by 54
  4. 2023Can Contextual Biasing Remain Effective with Whisper and GPT-2?cited by 21
  5. 2022Continuous Pseudo-Labeling from the Startcited by 11
  6. 2022Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Textcited by 1

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