Technical Program

Paper Detail

Paper:MLSP-P3.3
Session:Speech and Audio Processing
Time:Wednesday, May 19, 15:30 - 17:30
Presentation: Poster
Topic: Machine Learning for Signal Processing: Speech and Audio Processing Applications
Title: DYNAMIC BAYESIAN NETWORKS FOR MEETING STRUCTURING
Authors: Alfred Dielmann; University of Edinburgh 
 Steve Renals; University of Edinburgh 
Abstract: 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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