Human behavior state profile mapping based on recalibrated speech affective space model

People typically associate health with only physical health. However, health is also interconnected to mental and emotional health. People who are emotionally healthy are in control of their behaviors and experience better quality of life. Hence, understanding human behavior is very important in ensuring the complete understanding of one's holistic health. In this paper, we attempt to map human behavior state (HBS) profiles onto recalibrated speech affective space model (rSASM). Such an approach is derived from hypotheses that: 1) Behavior is influenced by emotion, 2) Emotion can be quantified through speech, 3) Emotion is dynamic and changes over time and 4) the emotion conveyance is conditioned by culture. Empirical results illustrated that the proposed approach can complement other types of behavior analysis in such a way that it offers more explanatory components from the perspective of emotion primitives (valence and arousal). Four different driving HBS; namely: distracted, laughing, sleepy and normal are profiled onto the rSASM to visualize the correlation between HBS and emotion. This approach can be incorporated in the future behavior analysis to envisage better performance.