Synthetic News Radio : content filtering and delivery for broadcast audio news

Abstract

Synthetic News Radio uses automatic speech recognition and clustered text news stories to automatically find story boundaries in an audio news broadcast, and it creates semantic representations that can match stories of similar content through audio-based queries. Current speech recognition technology cannot by itself produce enough information to accurately characterize news audio; therefore, the clustered text stories represent a knowledge base of relevant news topics that the system can use to combine recognition transcripts of short, intonational phrases into larger, complete news stories. Two interface mechanisms, a graphical desktop application and a touch-tone drive phone interface, allow quick and efficient browsing of the new structured news broadcasts. The system creates a personal, synthetic newscast by extracting stories, based on user interests, from multiple hourly newscasts and then reassembling them into a single recording at the end of the day. The system also supports timely delivery of important stories over a LAN or to a wireless audio pager. This thesis describes the design and evaluation of the news segmentation and content matching technology, and evaluates the effectiveness of the interface and delivery mechanisms.