CONF zhang-rr-05-31b/IDIAP Multimodal Integration for Meeting Group Action Segmentation and Recognition Al-Hames, Marc Dielmann, Alfred Gatica-Perez, Daniel Reiter, Stephan Renals, Steve Zhang, Dong EXTERNAL https://publications.idiap.ch/attachments/reports/2005/mlmi-05-joint.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/zhang-rr-05-31 Related documents MLMI 2005 IDIAP-RR 05-31 We address the problem of segmentation and recognition of sequences of multimodal human interactions in meetings. These interactions can be seen as a 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 multi-stream DBN, as well as a multi-stream mixed-state DBN for disturbed data. REPORT zhang-rr-05-31/IDIAP Multimodal Integration for Meeting Group Action Segmentation and Recognition Al-Hames, Marc Dielmann, Alfred Gatica-Perez, Daniel Reiter, Stephan Renals, Steve Zhang, Dong EXTERNAL https://publications.idiap.ch/attachments/reports/2005/rr-05-31.pdf PUBLIC Idiap-RR-31-2005 2005 IDIAP Martigny, Switzerland Published in ``MLMI'', July, 2005 We address the problem of segmentation and recognition of sequences of multimodal human interactions in meetings. These interactions can be seen as a 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 multi-stream DBN, as well as a multi-stream mixed-state DBN for disturbed data.