Using Audio and Video Features to Classify the Most Dominant Person in a Group Meeting

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.


Presented at:
""
Year:
2007
Note:
IDIAP-RR 07-29
Laboratories:




 Record created 2010-02-11, last modified 2018-03-17

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