Detecting Praising Behaviors Based on Multimodal Information

Abstract

Praising behavior is an important part of human communication. However, people who are unfamiliar with often praising have difficulty improving their praising skills. To solve this problem, we aim to construct a system for evaluating praising skills. So far, we have attempted to predict the degree of praising skills from verbal and nonverbal behaviors. However, our previous studies were focused on scenes in which the praiser was actually praising, and we have not dealt with scenes in which the praiser was not praising. In this paper, we attempt to detect whether the praiser is actually praising the receiver by including scenes in the study in which the praiser is not praising the receiver. First, we extract features related to the verbal and nonverbal behaviors of the praiser and receiver. Second, we construct machine learning models that utilize these features to detect whether or not the praiser is actually praising the receiver. Our results show that the machine learning model utilizing the acoustic and embedding-based linguistic behaviors of the praiser and the visual and acoustic behaviors of the receiver has the best detection performance.