Advancing African-Accented English Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models

Accents play a pivotal role in shaping human communication, enhancing our ability to convey and comprehend messages with clarity and cultural nuance.While there has been significant progress in Automatic Speech Recognition (ASR), African-accented English ASR has been understudied due to a lack of training datasets, which are often expensive to create and demand colossal human labor.By combining several active learning paradigms and the core-set approach, we propose a new multiround adaptation process that utilizes epistemic uncertainty to automate annotation, thereby significantly reducing associated costs and human labor.This novel method streamlines data annotation and strategically selects data samples that contribute most to model uncertainty, thereby enhancing training efficiency.We define a new U-WER metric to track model adaptation to hard accents.We evaluate our approach across several domains, datasets, and high-performing speech models.Our results show that our approach leads to a 27% WER relative average improvement while requiring, on average, 45% less data than established baselines.Our approach also improves out-of-distribution generalization for very low-resource accents, demonstrating its viability for building generalizable ASR models in the context of accented African ASR.We open-source the code here.