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The performance of speaker recognition system is highly dependent on theamount of speech used in enrollment and test. This work presents a detailedexperimental review and analysis of the GMM-SVM based speaker recognitionsystem in presence of duration variability. This article also reports acomparison of the performance of GMM-SVM classifier with its precursortechnique Gaussian mixture model-universal background model (GMM-UBM)classifier in presence of duration variability. The goal of this research workis not to propose a new algorithm for improving speaker recognition performancein presence of duration variability. However, the main focus of this work is onutterance partitioning (UP), a commonly used strategy to compensate theduration variability issue. We have analysed in detailed the impact of trainingutterance partitioning in speaker recognition performance under GMM-SVMframework. We further investigate the reason why the utterance partitioning isimportant for boosting speaker recognition performance. We have also shown inwhich case the utterance partitioning could be useful and where not. Our studyhas revealed that utterance partitioning does not reduce the data imbalanceproblem of the GMM-SVM classifier as claimed in earlier study. Apart fromthese, we also discuss issues related to the impact of parameters such asnumber of Gaussians, supervector length, amount of splitting required forobtaining better performance in short and long duration test conditions fromspeech duration perspective. We have performed the experiments with telephonespeech from POLYCOST corpus consisting of 130 speakers.

Utterance partitioning for speaker recognition: an experimental review and analysis with new findings under GMM-SVM framework