Extracting Speaker and Emotion Information from Self-Supervised Speech Models via Channel-Wise CorrelationsStafylakis Themos, Ladislav Mošner, Sofoklis Kakouros, OldÅ™ich Plchot, Lukáš Burget and L. Burget,

Omilia- Conversational Intelligence, Athens, Greece

Brno University of Technology, Faculty of Information Technology, Speech@FIT, Czechia

University of Helsinki, Finland



DOI. (10.1109/SLT54892.2023.10023345)

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.




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