Exploiting psychological factors for interaction style recognition in spoken conversation

Wen Li Wei, Chung Hsien Wu, Jen Chun Lin, Han Li

研究成果: Article同行評審

18 引文 斯高帕斯(Scopus)


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 3月

All Science Journal Classification (ASJC) codes

  • 聲學與超音波
  • 電氣與電子工程


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