|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
THE 1997 HTK BROADCAST NEWS TRANSCRIPTION SYSTEM
P.C. Woodland, T. Hain, S.E. Johnson, T.R. Niesler, A. Tuerk, E.W.D. Whittaker & S.J. Young
This paper presents the recent development of the HTK broadcast news transcription system. Previously we have used data type specific modelling based on adapted Wall Street Journal trained HMMs. However, we are now using data for which no manual pre-classification or segmentation is available and therefore automatic techniques are required and compatible acoustic modelling strategies must be adopted. A number of recognition experiments are presented that compare data-type specific and non-specific models; differing amounts of training data; the use of gender-dependent modelling and the effects of automatic data-type classification. Based on these experiments, the HTK system for the 1997 broadcast news evaluation was designed. A detailed description of this system is given which includes a class-based language modelling component. The complete system yields an overall word error rate of 22.0% on the 1996 unpartitioned broadcast news development test data and just 15.8% on the 1997 evaluation test set.
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2005 Cambridge University Engineering Dept
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