Robot emotional state through Bayesian visuo-auditory perception

In this paper we focus on auditory analysis as the sensory stimulus, and on vocalization synthesis as the output signal. Our scenario is to have one robot interacting with one human through vocalization channel. Notice that vocalization is far beyond speech; while speech analysis would give us what was said, vocalization analysis gives us how was said. A social robot shall be able to perform actions in different manners according to its emotional state. Thus we propose a novel Bayesian approach to determine the emotional state the robot shall assume according to how the interlocutor is talking to it. Results shows that the classification happens as expected converging to the correct decision after two iterations.