  Emphasizing a data-centric AI approach, this paper introduces a novel
two-stage active learning (AL) pipeline for automatic speech recognition (ASR),
combining unsupervised and supervised AL methods. The first stage utilizes
unsupervised AL by using x-vectors clustering for diverse sample selection from
unlabeled speech data, thus establishing a robust initial dataset for the
subsequent supervised AL. The second stage incorporates a supervised AL
strategy, with a batch AL method specifically developed for ASR, aimed at
selecting diverse and informative batches of samples. Here, sample diversity is
also achieved using x-vectors clustering, while the most informative samples
are identified using a Bayesian AL method tailored for ASR with an adaptation
of Monte Carlo dropout to approximate Bayesian inference. This approach enables
precise uncertainty estimation, thereby enhancing ASR model training with
significantly reduced data requirements. Our method has shown superior
performance compared to competing methods on homogeneous, heterogeneous, and
OOD test sets, demonstrating that strategic sample selection and innovative
Bayesian modeling can substantially optimize both labeling effort and data
utilization in deep learning-based ASR applications.
