Dielmann, Alfred

Renals, Steve

4

2006-05-09T14:36:01Z

2006-05-09T14:36:01Z

2004

This paper is about the automatic structuring of multiparty meetings using audio information. We have used a corpus of 53 meetings, recorded using a microphone array and lapel microphones for each participant. The task was to segment meetings into a sequence of meeting actions, or phases. We have adopted a statistical approach using dynamic Bayesian networks (DBNs). Two DBN architectures were investigated: a two-level hidden Markov model (HMM) in which the acoustic observations were concatenated; and a multistream DBN in which two separate observation sequences were modelled. Additionally we have also explored the use of counter variables to constrain the number of action transitions. Experimental results indicate that the DBN architectures are an improvement over a simple baseline HMM, with the multistream DBN with counter constraints producing an action error rate of 6%.

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Proc. IEEE ICASSP 2004

http://hdl.handle.net/1842/944

en

IEEE Signal Processing Society

Dynamic Bayesian Networks for Meeting Structuring

Conference Paper