Using audio and video features to classify the most dominant person in a group meeting
Hayley Hung, Dinesh Babu Jayagopi, Chuohao Yeo, Gerald H Friedland, Silèye Ba, Jean‐Marc Odobez, Kannan Ramchandran, Nikki Mirghafori, Daniel Gática-Pérez · 2007
The automated extraction of semantically meaningful information from multi-modal data is becoming increasingly necessary due to the escalation of captured data for archival. A novel area of multi-modal data labelling, which has received relatively little attention, is the automatic estimation of the most dominant person in a group meeting. In this paper, we provide a framework for detecting dominance in group meetings using different audio and video cues. We show that by using a simple model for dominance estimation we can obtain promising results.