Leader Identification Using Multimodal Information in Multi-party Conversations

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It is one of the important tasks to predict a participant's role in a multi-party conversation. Many previous studies utilized only verbal or non-verbal features to construct models for the role recognition task. In this paper, we propose a model that combines verbal and non-verbal features for leader identification. We add non-verbal features and construct our prediction model with utterance, pose, facial, and prosodic features. In our experiments, we compare our model with a baseline model that is based on only utterance features. The results show the effectiveness of our multimodal approach. In addition, we improve the performance of the baseline model to add some new utterance features.

International Conference on Asian Language Processing (IALP 2020), 4-6 December, 2020, Kuala Lumpur, Malaysia（新型コロナ感染拡大に伴い、オンライン開催に変更）

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