A comparison of SVM and asymmetric SIMPLS in emotion recognition from naturalistic dialogues

In this paper, we compare the performance of support vector machine (SVM) and asymmetric SIMPLS classifiers in emotion recognition from naturalistic dialogues. These two classifiers are evaluated on the SEMAINE corpus that involves emotional binary classification tasks of four dimensions, namely, activation, expectation, power, and valence. The experimental results reveal that the asymmetric SIMPLS (ASIMPLS) classifier is less sensitive to class distribution, faster training processing, and higher classification accuracy on the average unweight recall for develop sets than the baseline. Using the develop set, we provide analysis and simulation-based insights about the selection of the number of components for model validation of the ASIMPLS classifier. For the test sets, the performance of the ASIMPLS classifier achieves an absolute improvement of 6.10%, 6.14%, 24.45%, 1.32% on the weighted recall value on above-mentioned four dimensions, respectively, over the baseline model.