Towards user-independent classification of multimodal emotional signals

Abstract

Coping with differences in the expression of emotions is a challenging task not only for a machine, but also for humans. Since individualism in the expression of emotions may occur at various stages of the emotion generation process, human beings may react quite differently to the same stimulus. Consequently, it comes as no surprise that recognition rates reported for a user-dependent system are significantly higher than recognition rates for a user-independent system. Based on empirical data we obtained in our earlier work on the recognition of emotions from biosignals, speech and their combination, we discuss which consequences arise from individual user differences for automated recognition systems and outline how these systems could be adapted to particular user groups.