Interlocutor personality perception based on BFI profiles and coupled HMMs in a dyadic conversation

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Previous studies have found systematic associations between personality and individual differences in interpersonal communication. Recently, researchers used various features to analyze individual personality traits in speech, social media content and essays. While few studies focused on detecting the personality interaction between two interlocutors, this paper presents a new approach to automatically and simultaneously predict the personalities of two interlocutors in a dyadic conversation. First, the recurrent neural networks (RNNs) are adopted to project the linguistic features of the transcribed spoken text of the input speech to the Big Five Inventory (BFI) space. The Coupled hidden Markov models (coupled HMMs) are then used to predict the interlocutor personality from the transcribed text of the two speakers considering the conversational interaction in their dialogue turns. The Mandarin Conversational Dialogue Corpus (MCDC) was adopted to evaluate the performance on interlocutor personality perception. Experimental results show that the proposed approach achieved satisfactory results in predicting personalities of two interlocutors at the same time.

原文English
主出版物標題Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014
編輯Thomas Fang Zheng, Haizhou Li, Minghui Dong, Jianhua Tao, Yanfeng Lu
發行者Institute of Electrical and Electronics Engineers Inc.
頁面178-182
頁數5
ISBN(電子)9781479942206
DOIs
出版狀態Published - 2014 十月 24
事件9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014 - Singapore, Singapore
持續時間: 2014 九月 122014 九月 14

出版系列

名字Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014

Other

Other9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014
國家Singapore
城市Singapore
期間14-09-1214-09-14

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Software

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