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

• Type: Conference
• Year: 2008
• Where: ICMCS
• Authors: Alexander Haubold, John R. Kender
• Keywords: ICMCS 2008, Mel Frequency Cepstral Coefficient, Multimedia, Speaker Clustering, Unreported Effects