EXPERIMENTS IN BROADCAST NEWS TRANSCRIPTION
P.C. Woodland, T. Hain, S.E. Johnson, T.R. Niesler, A. Tuerk & 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 experimenting with data for which no manual pre-classification or segmentation is available and therefore automatic techniques are required and compatible acoustic modelling strategies adopted. An approach for automatic audio segmentation and classification is described and evaluated as well as extensions to our previous work on segment clustering. 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. It is shown that robust segmentation into a small number of audio types is possible and that models trained on a wide variety of data types can yield good performance.
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