|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SPOKEN DOCUMENT RETRIEVAL FOR TREC-8 AT CAMBRIDGE UNIVERSITY
S.E. Johnson , P. Jourlin, K. Sparck Jones & P.C. Woodland
This paper presents work done at Cambridge University on the TREC-8 Spoken Document Retrieval (SDR) Track. The 500 hours of broadcast news audio was filtered using an automatic scheme for detecting commercials, and then transcribed using a 2-pass HTK speech recogniser which ran at 13 times real time. The system gave an overall word error rate of 20.5% on the 10 hour scored subset of the corpus, the lowest in the track. Our retrieval engine used an Okapi scheme with traditional stopping and Porter stemming, enhanced with part-of-speech weighting on query terms, a stemmer exceptions list, semantic `poset' indexing, parallel collection frequency weighting, both parallel and traditional blind relevance feedback and document expansion using parallel blind relevance feedback. The final system gave an Average Precision of 55.29% on our transcriptions.
For the case where story boundaries are unknown, a similar retrieval system, without the document expansion, was run on a set of ``stories'' derived from windowing the transcriptions after removal of commercials. Boundaries were forced at ``commercial'' or ``music'' changes and some recombination of temporally close stories was allowed after retrieval. When scoring duplicate story hits and commercials as irrelevant, this system gave an Average Precision of 41.47% on our transcriptions.
The paper also presents results for cross-recogniser experiments using our retrieval strategies on transcriptions from our own first pass output, AT&T, CMU, 2 NIST-run BBN baselines, LIMSI and Sheffield University, and the relationship between performance and transcription error rate is shown.
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