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Recently, we introduced the sorted Gaussian mixture models (SGMMs) algorithm providing the means to tradeoff performance for operational speed and thus permitting the speed-up of GMM-based classification schemes. The performance of the SGMM algorithm depends on the proper choice of the sorting function, and the proper adjustment of its parameters. In the present work, we employ particle swarm optimization...
In this paper, we propose a hierarchical mixture clustering method and investigate its application for complexity reduction of a GMM based speaker identification system. We show that by using GMM-HMC one can cluster speakers more accurately than that of a sorted GMM with the same acceleration rate. The system was tested on a universal background model-Gaussian mixture model with KL-divergence as the...
In this paper we propose a new segmentation algorithm called delta MFCC based speech segmentation (DMFCC-SS), with application to speaker recognition systems. We show that DMFCC-SS can separate the regions of speech that result from similar likelihood scores using models such as a Gaussian mixture model (GMM), and can therefore be used to identify the regions of speech between two transitional states...
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