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ICMCS
2008
IEEE

Accommodating sample size effect on similarity measures in speaker clustering

8 years 10 months ago
Accommodating sample size effect on similarity measures in speaker clustering
We investigate the symmetric Kullback-Leibler (KL2) distance in speaker clustering and its unreported effects for differently-sized feature matrices. Speaker data is represented as Mel Frequency Cepstral Coefficient (MFCC) vectors, and features are compared using the KL2 metric to form clusters of speech segments for each speaker. We make two observations with respect to clustering based on
Alexander Haubold, John R. Kender
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICMCS
Authors Alexander Haubold, John R. Kender
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