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Speech emotion recognition (SER) has received a great deal of attention inrecent years in the context of spontaneous conversations. While there have beennotable results on datasets like the well known corpus of naturalistic dyadicconversations, IEMOCAP, for both the case of categorical and dimensionalemotions, there are few papers which try to predict both paradigms at the sametime. Therefore, in this work, we aim to highlight the performance contributionof multi-task learning by proposing a multi-task, multi-modal system thatpredicts categorical and dimensional emotions. The results emphasise theimportance of cross-regularisation between the two types of emotions. Ourapproach consists of a multi-task, multi-modal architecture that uses parallelfeature refinement through self-attention for the feature of each modality. Inorder to fuse the features, our model introduces a set of learnable bridgetokens that merge the acoustic and linguistic features with the help ofcross-attention. Our experiments for categorical emotions on 10-fold validationyield results comparable to the current state-of-the-art. In our configuration,our multi-task approach provides better results compared to learning eachparadigm separately. On top of that, our best performing model achieves a highresult for valence compared to the previous multi-task experiments.

A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions