Exploiting Turn Taking Temporal Evolution for Personality Trait Perception in Dyadic Conversations
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. The Mandarin 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.
IEEE/ACM Transactions on Audio, Speech, and Language Processing