Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models
arxiv(2023)
摘要
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. Combining several active learning paradigms and the core-set
approach, we propose a new multi-rounds adaptation process that uses epistemic
uncertainty to automate the annotation process, significantly reducing the
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 metric
called U-WER 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 69.44\% WER 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
\href{https://github.com/bonaventuredossou/active_learning_african_asr}{here}.
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