Data-Filtering Methods for Self-Training of Automatic Speech Recognition Systems

Abstract

Self-training is a simple and efficient way of leveraging un-labeled speech data: (i) start with a seed system trained on transcribed speech; (ii) pass the unlabeled data through this seed system to automatically generate transcriptions; (iii) en-large the initial dataset with the self-labeled data and retrain the speech recognition system. However, in order not to pol-lute the augmented dataset with incorrect transcriptions, an important intermediary step is to select those parts of the self-labeled data that are accurate. Several approaches have been proposed in the community, but most of the works address only a single method. In contrast, in this paper we inspect three distinct classes of data-filtering for self-training, leveraging: (i) confidence scores, (ii) multiple ASR hypotheses and (iii) approximate transcriptions. We evaluate these approaches from two perspectives: quantity vs. quality of the selected data and improvement of the seed ASR by including this data. The proposed methodology achieves state-of-the-art results on Romanian speech, obtaining 25% relative improvement over prior work. Among the three methods, approximate transcriptions bring the highest performance gain, even if they yield the least quantity of data.