Contrasting the Emotions identified in Spanish TV debates and in Human-Machine Interactions

Abstract

This work is aimed to contrast the similarities and differences for the emotions identified in two very different scenarios: human-to-human interaction on Spanish TV debates and human-machine interaction with a virtual agent in Spanish. To this end we developed a crowd annotation procedure to label the speech signal in terms of both, emotional categories and Valence-Arousal-Dominance models. The analysis of these data showed interesting findings that allowed to profile both the speakers and the task. Then, Convolutional Neural Networks were used for the automatic classification of the emotional samples in both tasks. Experimental results drew up a different human behavior in both tasks and outlined different speaker profiles.