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While there has been significant progress in ASR, African-accented clinicalASR has been understudied due to a lack of training datasets. Building robustASR systems in this domain requires large amounts of annotated or labeled data,for a wide variety of linguistically and morphologically rich accents, whichare expensive to create. Our study aims to address this problem by reducingannotation expenses through informative uncertainty-based data selection. Weshow that incorporating epistemic uncertainty into our adaptation roundsoutperforms several baseline results, established using state-of-the-art (SOTA)ASR models, while reducing the required amount of labeled data, and hencereducing annotation costs. Our approach also improves out-of-distributiongeneralization for very low-resource accents, demonstrating the viability ofour approach for building generalizable ASR models in the context of accentedAfrican clinical ASR, where training datasets are predominantly scarce.

Adapting Pretrained ASR Models to Low-resource Clinical Speech using Epistemic Uncertainty-based Data Selection