Automated Recognition of Affect and Stress evoked by Audio-Visual stimuli

In the present work, we consider the automated detection of negative emotions and high-arousal negative-valence (HANV) states, which are akin to acute stress occurring in specific context. We investigate the influence and intricacy which subjective perception of negative emotions and acute stress brings to the process of automated recognition of these states. For that purpose we experimentally evaluate the advantages of modelling based on the person-independent tags of the audio-video stimuli, and models built with the person-specific self-reported tags. Based on the self-reported tags, which are obtained with only tiny extra effort, we report a relative improvement of the HANV detection accuracy with up to 5%.