EmoSense: Automatically Sensing Emotions From Speech By Multi-way Classification

Reliably detecting emotions is a topic of current research in understanding mental health. Among the many modes of detecting emotion, audio has a prominent place. In this paper, we propose a two-level, multi-way classifier applied to classification of seven emotions from the standard Emo-DB database. The multi-way classifier is an automated methodology of analyzing a confusion matrix of a first-level classifier to build more classifiers at the next level. A random forest classifier is used on state-of-the-art features for analyzing affective speech. The confusion matrix from this classification level is analyzed to decide, for each class, which other classes are most confused by using a threshold on the misclassification rate. For the chosen pairs, second level classifiers are built and trained on the same data. Its performance on the training-set (73.3{\%) as well as a non-intersecting training set (72.9{\%) are both better than state-of-the-art performance. We initiate a possible explanation of the performance improvement by considering the confusion among emotions placed on Russel's circumplex model.