  Self-supervised learning of speech representations from large amounts of
unlabeled data has enabled state-of-the-art results in several speech
processing tasks. Aggregating these speech representations across time is
typically approached by using descriptive statistics, and in particular, using
the first- and second-order statistics of representation coefficients. In this
paper, we examine an alternative way of extracting speaker and emotion
information from self-supervised trained models, based on the correlations
between the coefficients of the representations - correlation pooling. We show
improvements over mean pooling and further gains when the pooling methods are
combined via fusion. The code is available at
github.com/Lamomal/s3prl_correlation.
