Multimodal Integration for Meeting Group Action Segmentation and Recognition

We address the problem of segmentation and recognition of sequences of multimodal human interactions in meetings. These interactions can be seen asa rough structure of a meeting, and can be used either as input for a meeting browser or as a first step towards a higher semantic analysis of the meeting. A common lexicon of multimodal group meeting actions, a shared meeting data set, and a common evaluation procedure enable us to compare the different approaches. We compare three different multimodal feature sets and four modelling infrastructures: a higher semantic feature approach, multi-layer HMMs, a multistream DBN, as well as a multi-stream mixed-state DBN for disturbed data.