  Many speech applications require understanding aspects beyond the words being
spoken, such as recognizing emotion, detecting whether the speaker is wearing a
mask, or distinguishing real from synthetic speech. In this work, we introduce
a new state-of-the-art paralinguistic representation derived from large-scale,
fully self-supervised training of a 600M+ parameter Conformer-based
architecture. We benchmark on a diverse set of speech tasks and demonstrate
that simple linear classifiers trained on top of our time-averaged
representation outperform nearly all previous results, in some cases by large
margins. Our analyses of context-window size demonstrate that, surprisingly, 2
second context-windows achieve 96\% the performance of the Conformers that use
the full long-term context on 7 out of 9 tasks. Furthermore, while the best
per-task representations are extracted internally in the network, stable
performance across several layers allows a single universal representation to
reach near optimal performance on all tasks.
