DBN Based Joint Dialogue Act Recognition of Multiparty Meetings
Alfred Dielmann, Steve J. Renals · 2007
Joint dialogue act segmentation and classification of the new AMI meeting corpus has been performed through an integrated framework based on a switching dynamic Bayesian network and a set of continuous features and language models. The recognition process is based on a dictionary of 15 DA classes tailored for group decision-making. Experimental results show that a novel interpolated factored language model results in a low error rate on the automatic segmentation task, and thus good recognition results can be achieved on AMI multiparty conversational speech.