|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
AUDIO INDEXING AND RETRIEVAL OF COMPLETE BROADCAST NEWS SHOWS
S.E. Johnson , P. Jourlin, K. Sparck Jones & P.C. Woodland
This paper describes a system for retrieving relevant portions of complete broadcast news shows starting with only the audio data. A novel system of automatically detecting and removing commercials is described and shown to increase the performance of the system whilst also reducing the computational effort required. The sophisticated large vocabulary speech recogniser which produces the high-quality transcriptions and the window-based retrieval system with post-merging are also described.
Results are presented using the 1999 TREC-8 Spoken Document Retrieval data for the task where no story boundaries are known. Experiments investigating the effectiveness of all aspects of the system are described and the relative benefits of automatically eliminating commercials, enforcing broadcast structure during retrieval, using relevance feedback, changing retrieval parameters and merging during post-processing are shown. An Average Precision of 46.5\%, when duplicates are scored as irrelevant is shown to be achievable using this system.
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