|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SEGMENTATION AND CLASSIFICATION OF BROADCAST NEWS AUDIO
T. Hain, P.C. Woodland
Broadcast news audio data contains a wide variety of different speakers and audio conditions (channel and background noise). This paper describes a segmentation, gender detection and audio classification scheme for such data which aims to provide a speech recogniser with a stream of reasonably-sized segments, each from a single speaker and audio type while discarding non-speech data. Each segment is labelled as either narrow or wide band and from either a female or male speaker. The segmentation system has been evaluated on the DARPA 1997 broadcast news data set and detailed segmentation accuracy results are presented. It is shown that the speech recognition accuracy for these automatically derived segments is very nearly the same as that for manually segmented data.
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2005 Cambridge University Engineering Dept
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