New Directions in Emotion Theory

Abstract

Emotional intelligence is a fundamental component towards a complete and natural interaction between human and machine. Towards this goal several emotion theories have been exploited in the affective computing domain. Along with the studies developed in the theories of emotion, there are two major approaches to characterize emotional models: categorical models and dimensional models. Whereas, categorical models indicate there are a few basic emotions that are independent on the race (e.g. Ekman's model), dimensional approaches suggest that emotions are not independent, but related to one another in a systematic manner (e.g. Circumplex of Affect). Although these models have been dominating in the affective computing research, recent studies in emotion theories have shown that these models only capture a small fraction of the variance of what people perceive. In this talk, I will present the new directions in emotion theory that can better capture the emotional behavior of individuals. First, I will discuss the statistical analysis behind key emotions that are conveyed in human vocalizations, speech prosody, and facial expressions, and how these relate to conventional categorical and dimensional models. Based on these new emotional models, I will describe new datasets we have collected at Hume AI, and show the different patterns captured when training deep neural network models.