This paper is concerned with automatic classification of broadcast news stories based on speaker roles such as anchor, reporter and others. The story classification is the first step for many related tasks such as browsing, indexing, and summarising the news broadcast. We use broadcast news audio and its automatic speech recogniser transcripts to implement the classification system. It builds on speaker segmentation and identification, story segmentation and named entity identification. It has achieved 92% accuracy when individual stories were provided manually. The performance declined to 67% and 51%, of precision and recall related measures respectively, when combined with automatic story boundary segmentation.
Bibliographic reference. Kolluru, BalaKrishna / Gotoh, Yoshihiko (2007): "Speaker role based structural classification of broadcast news stories", In INTERSPEECH-2007, 2593-2596.