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This paper investigates the automatic segmentation of meetings into a sequence of group actions or phases. Our work is based on a corpus of multiparty meetings collected in a meeting room instrumented with video cameras, lapel microphones and a microphone array. We have extracted a set of feature streams, in this case extracted from the audio data, based on speaker turns, prosody and a transcript of what was spoken. We have related these signals to the higher level semantic categories via a multistream statistical model based on dynamic Bayesian networks (DBNs). We report on a set of experiments in which different DBN architectures are compared, together with the different feature streams. The resultant system has an action error rate of 9%.