Segmentation and classification of meeting events using multiple classifier fusion and dynamic programming

In this paper, the segmentation of a meeting into meeting events is investigated as well as the recognition of the detected segments. First, the classification of a meeting event is examined. Five different classifiers are combined through multiple classifier fusion. Then, a way for finding the optimal segment boundaries is presented. With a dynamic programming approach quite encouraging results can be obtained. The results show further that by classifier fusion a more stable result can be achieved than using only one single classifier.