Annotation and Classification of Changes of Involvement in Group Conversation

The detection of involvement in a conversation is important to assess the level humans are participating in either a human-human or human-computer interaction. Especially, detecting changes in a group's involvement in a multi-party interaction is of interest to distinguish several constellations in the group itself. This information can further be used in situations where technical support of meetings is favoured, for instance, focusing a camera, switching microphones, etc. Moreover, this information could also help to improve the performance of technical systems applied in human-machine interaction. In this paper, we concentrate on video material given by the Table Talk corpus. Therefore, we introduce a way of annotating and classifying changes of involvement and discuss the reliability of the annotation. Further, we present classification results based on video features using Multi-Layer Networks.