  Many semi- and weakly-supervised approaches have been investigated for
overcoming the labeling cost of building high quality speech recognition
systems. On the challenging task of transcribing social media videos in
low-resource conditions, we conduct a large scale systematic comparison between
two self-labeling methods on one hand, and weakly-supervised pretraining using
contextual metadata on the other. We investigate distillation methods at the
frame level and the sequence level for hybrid, encoder-only CTC-based, and
encoder-decoder speech recognition systems on Dutch and Romanian languages
using 27,000 and 58,000 hours of unlabeled audio respectively. Although all
approaches improved upon their respective baseline WERs by more than 8%,
sequence-level distillation for encoder-decoder models provided the largest
relative WER reduction of 20% compared to the strongest data-augmented
supervised baseline.
