Hamid Reza Sadegh Mohammadi

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In this paper a new structured Gaussian mixture model, called sorted GMM, is proposed as an efficient method to implement GMM-based speaker verification systems; such as Gaussian mixture model universal background model (GMM-UBM) scheme. The proposed method uses a sorted GMM which facilitate partial search and has lower computational complexity and less(More)
Gaussian selection is a technique applied in the GMM-UBM framework to accelerate score calculation. We have recently introduced a novel Gaussian selection method known as sorted GMM (SGMM). SGMM uses scalar-indexing of the universal background model mean vectors to achieve fast search of the top-scoring Gaussians. In the present work we extend this method(More)
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(More)
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