Unsupervised topic model for broadcast program segmentation

Several unsupervised methods have been proposed to segment a continuous text stream into individual topics. A simple HMM formulation of the most successful of these methods exposes their underlying assumptions and suggests the use of a new prior for segmentation probability. Under this formulation, we explore the space of possible modeling choices on databases of English and French TV and radio programs. We show that the proposed prior improves segmentation results and can also accommodate additional knowledge sources within the HMM efficient dynamic programming.