Yukiko Nakano

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.

Eye gaze has been used broadly in interactive intelligent systems. The research area has grown in recent years to cover emerging topics that go beyond the traditional focus on interaction between a single user and an interactive system. This special issue presents five articles that explore new directions of gaze-based interactive intelligent systems, ranging from communication robots in dyadic and multiparty conversations to a driving simulator that uses eye gaze evidence to critique learners' behavior.