  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 KL2: 1.) The accuracy of clustering is strongly dependent
on the absolute lengths of the speech segments and their extracted feature
vectors. 2.) The accuracy of the similarity measure strongly degrades with the
length of the shorter of the two speech segments. These effects of length can
be attributed to the measure of covariance used in KL2. We demonstrate an
empirical correction of this sample-size effect that increases clustering
accuracy. We draw parallels to two Vector Quantization-based (VQ) similarity
measures, one which exhibits an equivalent effect of sample size, and the
second being less influenced by it.
