Multimodal emotion recognition in audiovisual communication

This paper discusses innovative techniques to automatically estimate a user's emotional state analyzing the speech signal and haptical interaction on a touch-screen or via mouse. The knowledge of a user's emotion permits adaptive strategies striving for a more natural and robust interaction. We classify seven emotional states: surprise, joy, anger, fear, disgust, sadness, and neutral user state. The user's emotion is extracted by a parallel stochastic analysis of his spoken and haptical machine interactions while understanding the desired intention. The introduced methods base on the common prosodic speech features pitch and energy, but rely also on the semantic and intention based features wording, degree of verbosity, temporal intention and word rate, and finally the history of user utterances. As further modality even touch-screen or mouse interaction is analyzed. The estimates based on these features are integrated in a multimodal way. The introduced methods base on results of user studies. A realization proved to be reliable compared with subjective probands' impressions.