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

翻译：近年来，基于语音的自监督学习（SSL）在包括自动语音识别（ASR）在内的多项任务中取得了显著进展。通过使用少量标注数据微调SSL模型，即可实现性能尚可的ASR模型。降低对标注数据的需求始终具有重要的实际价值。本文进一步将SSL的应用扩展到主动学习中以削减标注成本。我们通过无监督方式从语音信号中提取三种不同粒度的单元，并应用对比数据选择方法比较其效果。实验结果表明，与随机选择相比，我们提出的数据选择框架在使用相同标注数据量时，可将词错误率（WER）有效提升超过11%，或在保持相同WER的情况下将标注成本减半。