Proposal of a Model to Determine the Attention Target for an Agent in Group Discussion with Non-verbal Features

In recent years, companies are seeking for communication skill from their employers. More and more companies adopt group discussions in employer recruitment to evaluate the ap- plicants' communication skill. However, the opportunity to improve communication skill in group discussion is limited due to the lack of partners. In order to solve this issue, our ongoing project is aiming to build a virtual agent or a robot that can participate group discussion, so that its users can re- peatedly practice group discussion with it. In this paper, we propose the models in directing the agent's attention toward the other participants in three situations:when the agent is speaking, when the agent is listening, and when no partic- ipant is speaking. First, we gathered a data corpus of the discussion of 10 four-people groups. We then use low-level non-verbal features including attention of other participant, voice prosody, head movements, and speech turn extracted in the 10-hour corpus to train support vector machine models to determine the agent's attention on the other participants, or the material. The performance of the detection models in F-measure range between 0.4 and 0.6.