Improving ASR-based topic segmentation of TV programs with confidence measures and semantic relations

The increasing quantity of video material requires methods to help users navigate such data, among which topic segmentation techniques. The goal of this article is to improve ASRbased topic segmentation methods to deal with peculiarities of professional-video transcripts (transcription errors and lack of repetitions) while remaining generic enough. To this end, we introduce confidence measures and semantic relations in a segmentation method based on lexical cohesion. We show significant improvements of the F1-measure when integrating confidence measures and semantic relations respectively. Such improvement demonstrates that simple clues can conteract errors in automatic transcripts and lack of repetitions.