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

arXiv:2306.02105v3 Announce Type: replace
Abstract: Accents are crucial in human communication as they help us understand others and allow us to communicate intelligibly in a way others understand us. While there has been significant progress in ASR, African-accented ASR has been understudied due to a lack of training datasets which are often expensive to create and demand colossal human labor. Our study aims to address this problem by automating the annotation process and reducing annotation-related expenses through informative uncertainty-based data …

arxiv asr cs.cl cs.sd data driven data eess.as recognition speech speech recognition type uncertainty