DBN BASED JOINT DIALOGUE ACT RECOGNITION OF MULTIPARTY MEETINGS

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