Estimating Persuasiveness in Group Discussions

Abstract

There are many opportunities that people make decisions through group discussions, and persuasiveness is one of the important skills to take advantage during the discussion. With the goal of estimating the persuasiveness of conversation participants, first, we collected a group discussion corpus and annotated persuasiveness of each participants for one-minute intervals. Then, using the dataset, we created GRU-based neural network models that estimate the participant’s persuasiveness from speech, language, and visual information. Comparing unimodal and multimodal models, we found that a multimodal model that combines language and audio information performed best, and the accuracy was 0.5625.