  Self-Supervised Learning (SSL) from speech data has produced models that have
achieved remarkable performance in many tasks, and that are known to implicitly
represent many aspects of information latently present in speech signals.
However, relatively little is known about the suitability of such models for
prosody-related tasks or the extent to which they encode prosodic information.
We present a new evaluation framework, SUPERB-prosody, consisting of three
prosody-related downstream tasks and two pseudo tasks. We find that 13 of the
15 SSL models outperformed the baseline on all the prosody-related tasks. We
also show good performance on two pseudo tasks: prosody reconstruction and
future prosody prediction. We further analyze the layerwise contributions of
the SSL models. Overall we conclude that SSL speech models are highly effective
for prosody-related tasks.
