  A novel text-independent speaker identification (SI) method is proposed. This
method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic
information among adjacent frames as feature sets to capture speaker's
characteristics. In order to utilize dynamic information, we design super-MFCCs
features by cascading three neighboring MFCCs frames together. The probability
density function (PDF) of these super-MFCCs features is estimated by the
recently proposed histogram transform~(HT) method, which generates more
training data by random transforms to realize the histogram PDF estimation and
recedes the commonly occurred discontinuity problem in multivariate histograms
computing. Compared to the conventional PDF estimation methods, such as
Gaussian mixture models, the HT model shows promising improvement in the SI
performance.
