Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In spite of these advances, to the best of our knowledge, there is no analysis of how the composition of the labelled and unlabelled datasets used in these methods affects the results. In this work we aim to analyse the effect of number of speakers in the training data on a recent SSL algorithm (wav2vec 2.0), and a recent ST algorithm (slimIPL). We perform a systematic analysis on both labeled and unlabeled data by varying the number of speakers while keeping the number of hours fixed and vice versa. Our findings suggest that SSL requires a large amount of unlabeled data to produce high accuracy results, while ST requires a sufficient number of speakers in the labelled data, especially in the low-regime setting. In this manner these two approaches improve supervised learning in different regimes of data composition.

翻译：自训练（ST）与自监督学习（SSL）方法已在自动语音识别（ASR）领域展现出显著性能提升。然而，据我们所知，目前尚无研究分析这些方法中标记与未标记数据集的组成如何影响实验结果。本研究旨在分析训练数据中说话人数量对最新SSL算法（wav2vec 2.0）及最新ST算法（slimIPL）的影响。我们通过固定小时数改变说话人数、固定说话人数改变小时数的系统化分析，系统研究了标记数据与未标记数据的影响。研究结果表明，SSL需要大量未标记数据才能获得高精度结果，而ST则要求标记数据中具有充足数量的说话人，尤其是在低资源场景下。由此可知，这两种方法在不同数据组成条件下均能提升监督学习性能。