SPOKEN DOCUMENT RETRIEVAL FOR TREC-7 AT CAMBRIDGE UNIVERSITY
S.E. Johnson , P. Jourlin, G.L. Moore, K. Sparck Jones & P.C. Woodland
This paper presents work done at Cambridge University, on the TREC-7 Spoken Document Retrieval (SDR) Track. The broadcast news audio was transcribed using a 2-pass gender-dependent HTK speech recogniser which ran at 50 times real time and gave an overall word error rate of 24.8\%, the lowest in the track. The Okapi-based retrieval engine used in TREC-6 by the City/Cambridge University collaboration was supplemented by improving the stop-list, adding a bad-spelling mapper and stemmer exceptions list, adding word-pair information, integrating part-of-speech weighting on query terms and including some pre-search statistical expansion. The final system gave an average precision of 0.4817 on the reference and 0.4509 on the automatic transcription, with the R-precision being 0.4603 and 0.4330 respectively.
The paper also presents results on a new set of 60 queries with assessments for the TREC-6 test document data used for development purposes, and analyses the relationship between recognition accuracy, as defined by a pre-processed term error rate, and retrieval performance for both sets of data.
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