  The performance of speaker recognition system is highly dependent on the
amount of speech used in enrollment and test. This work presents a detailed
experimental review and analysis of the GMM-SVM based speaker recognition
system in presence of duration variability. This article also reports a
comparison of the performance of GMM-SVM classifier with its precursor
technique Gaussian mixture model-universal background model (GMM-UBM)
classifier in presence of duration variability. The goal of this research work
is not to propose a new algorithm for improving speaker recognition performance
in presence of duration variability. However, the main focus of this work is on
utterance partitioning (UP), a commonly used strategy to compensate the
duration variability issue. We have analysed in detailed the impact of training
utterance partitioning in speaker recognition performance under GMM-SVM
framework. We further investigate the reason why the utterance partitioning is
important for boosting speaker recognition performance. We have also shown in
which case the utterance partitioning could be useful and where not. Our study
has revealed that utterance partitioning does not reduce the data imbalance
problem of the GMM-SVM classifier as claimed in earlier study. Apart from
these, we also discuss issues related to the impact of parameters such as
number of Gaussians, supervector length, amount of splitting required for
obtaining better performance in short and long duration test conditions from
speech duration perspective. We have performed the experiments with telephone
speech from POLYCOST corpus consisting of 130 speakers.
