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In recent years, speech-based self-supervised learning (SSL) has madesignificant progress in various tasks, including automatic speech recognition(ASR). An ASR model with decent performance can be realized by fine-tuning anSSL model with a small fraction of labeled data. Reducing the demand forlabeled data is always of great practical value. In this paper, we furtherextend the use of SSL to cut down labeling costs with active learning. Threetypes of units on different granularities are derived from speech signals in anunsupervised way, and their effects are compared by applying a contrastive dataselection method. The experimental results show that our proposed dataselection framework can effectively improve the word error rate (WER) by morethan 11% with the same amount of labeled data, or halve the labeling cost whilemaintaining the same WER, compared to random selection.

Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition