Multimodal Emotion Classification in Naturalistic User Behavior
The design of intelligent personalized interactive systems, having knowledge about the user’s state, his desires, needs and wishes, currently poses a great challenge to computer scientists. In this study we propose an information fusion approach combining acoustic, and bio-physiological data, comprising multiple sensors, to classify emotional states. For this purpose a multimodal corpus has been created, where subjects undergo a controlled emotion eliciting experiment, passing several octants of the valence arousal dominance space. The temporal and decision level fusion of the multiple modalities outperforms the single modality classifiers and shows promising results.
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