DANCERS: Delft advanced news retrieval system

In this paper we present a system for automated analysis, classification and indexing of broadcast news programs. The system first analyses the visual and the speech stream of an input news program in order to obtain an initial partitioning of the program into the so-called report segments. The analysis of the visual stream provides the boundaries of the report segments lying at the beginning and the end of each anchorperson shot. This analysis step is performed by applying an existing techniques for anchorperson shot detection. The analysis of the speech stream gives the boundaries of the report segments lying in the middle of each silent interval. Then, the transcribed speech of each of the report segments is matched with the content of a large pre-specified textual topic database. This database covers a large number of topics and can be updated by the user at any time. Fro each topic a vast number of keywords is given, each of which is also assigned a weight that indicates the importance of a keyword for a certain topic. The result of the matching procedure is a list of probable topics per report segment, where for each topic on the list a likelihood is computed based on the number of relevant keywords found in the segment and on the weights of those keywords. The list of topics per segment is then shortened by separating the most probable from least probable topics based on their likelihood. Finally, the likelihood values of the most probable topics are used in the last system module to merge related neighboring segments into reports. The most probable topics serving as the base for the segment-merging procedure are the same time the retrieval indexes for the reports and are used for classifying together all reports in the database that cover one and the same topic.