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 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 recognition and also demonstrate that combining psychological factors effectively improves IS recognition accuracy.
|頁（從 - 到）||659-671|
|期刊||IEEE Transactions on Audio, Speech and Language Processing|
|出版狀態||Published - 2014 三月 1|
All Science Journal Classification (ASJC) codes
- Acoustics and Ultrasonics
- Electrical and Electronic Engineering