Computational Modeling of Human Emotion

Emotion has intrigued researchers for generations. This fascination has permeated the engineering community, motivating the development of affective computational models for classification. However, human emotion remains notoriously difficult to interpret both because of the mismatch between the emotional cue generation (the speaker) and cue perception (the observer) processes and because of the presence of complex emotions, emotions that contain shades of multiple affective classes. The goals of my research are motivated by these complexities. I study methods to provide a computational account of how humans perceive emotional utterances (“emotion perception”) and combine this with knowledge gleaned from emotion estimation studies (“emotion recognition”) to develop a system capable of interpreting naturalistic expressions of emotion utilizing a new quantification measure (“emotion representation”). The focus of this research is to provide a computational description of human emotion perception and combine this knowledge with the information gleaned from emotion classification experiments to develop representations that are both human and machine interpretable. Proper representation and quantification will support the development of affective assistive technologies, new algorithmic development, and will further our understanding of the emotion production and perception processes.

CSE Faculty

Mower Provost, Emily