  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.
