Abstract
It is a central challenge in Affective Computing to estimate rapport from verbal/nonverbal behaviors in conversation using machine learning models. Recently, it has been reported that the predictive performance of rapport can be improved by taking the speaker's personality into account. However, it is not fully clear why personality contributes to the improvement of rapport prediction. First, we developed a regression model to predict subjective rapport from verbal/nonverbal features in conversation. We then examined the effectiveness of combining the personality traits generated from the BigFive questionnaire with the verbal/nonverbal features. We also applied the Social Relations Model, an analytical model of interpersonal perception, to analyze the predictive value of the machine learning model, and investigated the effect of adding personality features on the model in detail. Experimental results showed that the addition of personality features improved the predictive performance of rapport in the model using facial expression features. Furthermore, our analysis suggests that the improvement in the predictive performance of the rapport by personality features may be due to the implicit improvement in the predictive performance of the perceiver effect and the relationship effect.
