Automatic story segmentation for spoken document retrieval

We have been working on speech retrieval based on Cantonese television news programs. Our video archive contains over 20 hours of news programs provided by a local television station. These programs have been hand-segmented into video clips, where each clip is a self-contained news story. The audio tracks in our archive are indexed by Cantonese speech recognition. This is integrated with a vector-space information retrieval model to achieve speech retrieval. This paper proposes an approach for automatic story segmentation from television news programs, intended to replace hand-segmentation as described above. Automatic story segmentation is critical for rapid expansion of our video archive. Our approach relies on the assumption that nearly all the news stories follow the temporal syntax of (begin-story /spl rarr/ anchor shots /spl rarr/ field shots /spl rarr/ end-story). Therefore, our algorithm aims to detect field-to-anchor shot boundaries that should also coincide with the story boundaries. The proposed approach utilizes the video frame information for story boundary detection, and involves such techniques as fuzzy c-means and graph-theoretical clustering. The approach achieved precision and recall values of over 70%, based on a 20-hour video corpus.