|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
BROADCAST NEWS TRANSCRIPTION USING HTK
P.C. Woodland, M.J.F. Gales, D. Pye & S.J. Young
This paper examines the issues in extending a large vocabulary speech recognition system designed for clean and noisy read speech tasks to handle broadcast news transcription. Results using the 1995 DARPA H4 evaluation data set are presented for different front-end analyses and use of unsupervised model adaptation using maximum likelihood linear regression (MLLR). The HTK system for the 1996 H4 evaluation is then described. It includes a number of new features over previous HTK large vocabulary systems including decoder-guided segmentation, segment clustering, cache-based language modelling, and combined MAP and MLLR adaptation. The system runs in multiple passes through the data and the detailed results of each pass are given.
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