  The cost of annotating transcriptions for large speech corpora becomes a
bottleneck to maximally enjoy the potential capacity of deep neural
network-based automatic speech recognition models. In this paper, we present a
new training pipeline boosting the conventional active learning approach
targeting label-efficient learning to resolve the mentioned problem. Existing
active learning methods only focus on selecting a set of informative samples
under a labeling budget. One step further, we suggest that the training
efficiency can be further improved by utilizing the unlabeled samples,
exceeding the labeling budget, by introducing sophisticatedly configured
unsupervised loss complementing supervised loss effectively. We propose new
unsupervised loss based on consistency regularization, and we configure
appropriate augmentation techniques for utterances to adopt consistency
regularization in the automatic speech recognition task. From the qualitative
and quantitative experiments on the real-world dataset and under real-usage
scenarios, we show that the proposed training pipeline can boost the efficacy
of active learning approaches, thus successfully reducing a sustainable amount
of human labeling cost.
