Exploiting Psychological Factors for Interaction Style Recognition in Spoken Conversation

Determining how a speaker is engaged in a conversation is crucial for achieving harmonious interaction between computers and humans. In this study, a fusion approach was developed based on psychological factors to recognize Interaction Style ($IS$ ) in spoken conversation, which plays a key role in creating natural dialogue agents. The proposed Fused Cross-Correlation Model (FCCM) provides a unified probabilistic framework to model the relationships among the psychological factors of emotion, personality trait ($PT$), transient $IS$, and $IS$ history, for recognizing $IS$. An emotional arousal-dependent speech recognizer was used to obtain the recognized spoken text for extracting linguistic features to estimate transient $IS$ likelihood and recognize $PT$. A temporal course modeling approach and an emotional sub-state language model, based on the temporal phases of an emotional expression, were employed to obtain a better emotion recognition result. The experimental results indicate that the proposed FCCM yields satisfactory results in $IS$ recognition and also demonstrate that combining psychological factors effectively improves $IS$ recognition accuracy.