|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
CLUSTER VOTING FOR SPEAKER DIARISATION
S. E. Tranter
It is often important to be able to automatically detect 'who spoke when' in audio data. The speaker diarisation task attempts to address this problem on Broadcast News data by defining an error rate which can be used to evaluate segmentations and their associated speaker labels. Many different methods exist to automatically generate such segmentations and it would be desirable if segmentations from different origins could be combined to produce a more accurate one. This paper introduces a cluster voting scheme which attempts to use information from more than one diarisation system to produce a new speaker segmentation with a lower diarisation error rate.
The scheme first generates a set of possible segmentations which minimise a distance metric based on the diarisation error rate and then defines a method of picking the final output from this set. Experiments presented using two inputs confirm that the diarisation error rate can be reduced using this new method.
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2005 Cambridge University Engineering Dept
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