Exploiting turn-taking temporal evolution for personality trait perception in dyadic conversations

Ming Hsiang Su, Chung Hsien Wu, Yu Ting Zheng

研究成果: Article同行評審

22 引文 斯高帕斯(Scopus)


In dyadic conversations, turn-taking is a dynamically evolving behavior strongly linked to paralinguistic communication. Turn-taking temporal evolution in a dyadic conversation is inevitable and can be incorporated into a modeling framework for characterizing and recognizing the personality traits (PTs) of two speakers. This study presents an approach to automatically predicting PTs in a dyadic conversation. First, a recurrent neural network (RNN) was used to model the relationship between Big Five Inventory 10 (BFI-10) items and linguistic features of spoken text in each turn of a speaker (speaker turn) to output a BFI-10 profile. The RNN applies a recurrent property to characterize the short-term temporal evolution of a dialog. Second, the coupled hidden Markov model (C-HMM) was employed to model the long-term turn-taking temporal evolution and cross-speaker contextual information for detecting the PTs of two individuals for the entire dialog represented by the BFI-10 profile sequence. TheMandarin Conversational Dialogue Corpus was used for evaluation. The evaluation result shows that an average perception accuracy of 79.66% for the big five traits was achieved using five-fold cross validation. Compared with conventional HMM and support vector machine-based methods, the proposed approach achieved a more favorable performance according to a statistical significance test. The encouraging results confirm the usability of this system for future applications.

頁(從 - 到)733-744
期刊IEEE/ACM Transactions on Audio Speech and Language Processing
出版狀態Published - 2016 4月

All Science Journal Classification (ASJC) codes

  • 電腦科學(雜項)
  • 聲學與超音波
  • 計算數學
  • 電氣與電子工程


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