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.