Semantic Segmentation of Radio Programs using Social Network Analysis and Duration Distribution Modeling

This work presents and compare two approaches for the se- mantic segmentation of broadcast news: the first is based on Social Network Analysis, the second is based on Pois- son Stochastic Processes. The experiments are performed over 27 hours of material: preliminary results are obtained by addressing the problem of splitting different episodes of the same program into two parts corresponding to a news bul- letin and a talk-show respectively. The results show that th e transition point between the two parts can be detected with an average error of around three minutes, i.e. roughly 5 percent of each episode duration.