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Self-supervised learning of speech representations from large amounts ofunlabeled data has enabled state-of-the-art results in several speechprocessing tasks. Aggregating these speech representations across time istypically approached by using descriptive statistics, and in particular, usingthe first- and second-order statistics of representation coefficients. In thispaper, we examine an alternative way of extracting speaker and emotioninformation from self-supervised trained models, based on the correlationsbetween the coefficients of the representations - correlation pooling. We showimprovements over mean pooling and further gains when the pooling methods arecombined via fusion. The code is available atgithub.com/Lamomal/s3prl_correlation.

Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations