A Practitioner's Guide to Building ASR Models for Low-Resource Languages: A Case Study on Scottish Gaelic

• URL: http://arxiv.org/abs/2506.04915v1
• Date: Thu, 05 Jun 2025 11:52:08 GMT
• Title: A Practitioner's Guide to Building ASR Models for Low-Resource Languages: A Case Study on Scottish Gaelic
• Authors: Ondřej Klejch, William Lamb, Peter Bell,
• Abstract summary: An effective approach to the development of ASR systems for low-resource languages is to fine-tune an existing multilingual end-to-end model.<n>We show that an approach combining hybrid HMMs with self-supervised models can yield substantially better performance with limited training data.<n>We benchmark our approach on Scottish Gaelic, achieving WER reductions of 32% relative over our best fine-tuned Whisper model.
• Score: 9.380879437204277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An effective approach to the development of ASR systems for low-resource languages is to fine-tune an existing multilingual end-to-end model. When the original model has been trained on large quantities of data from many languages, fine-tuning can be effective with limited training data, even when the language in question was not present in the original training data. The fine-tuning approach has been encouraged by the availability of public-domain E2E models and is widely believed to lead to state-of-the-art results. This paper, however, challenges that belief. We show that an approach combining hybrid HMMs with self-supervised models can yield substantially better performance with limited training data. This combination allows better utilisation of all available speech and text data through continued self-supervised pre-training and semi-supervised training. We benchmark our approach on Scottish Gaelic, achieving WER reductions of 32% relative over our best fine-tuned Whisper model.

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