A Multiparty Model for Estimating Persuasiveness in Group Discussions

Abstract

Persuasiveness is an important communication skill in communicating with others. This study aims to estimate the persuasiveness of the participants in group discussions. First, human annotators rated the level of persuasiveness of each of four participants in group discussions. Next, GRU-based neural networks were used to create speech, verbal, and visual (head pose) encoders. The output from each encoder was combined to create a multimodal and multiparty model to estimate the persuasiveness of each participant. The experiment results showed that multimodal and multiparty models are better than unimodal and single-person models. The best performing multimodal multiparty model achieved 80% accuracy in predicting high/low persuasiveness, and 77% accuracy in predicting the most persuasive participant in the group.