An Improved Speaker Identification System Using Automatic Split-Merge Incremental Learning (A-SMILE) of Gaussian Mixture Models

In this paper, a new model-based clustering algorithm is introduced for optimal speaker modeling in speaker identification systems. The introduced algorithm can estimates the optimal number of mixture components using a cross-validation methodology, as well as, overcome the initialization sensitivity and local maxima problems of classical EM algorithm using a split & merge incremental learning approach. The performed experiments in speaker identification task demonstrate the efficiency and effectivity of the proposed algorithm compared to the commonly used Expectation-Maximization (EM) algorithm.