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)
Conventional Speaker Identification(SI) Systems uses individual Gaussian Mixture Models(GMM) for every speaker. If this method used for the large population Speaker identification systems, then during identification, likelihood computations between an unknown speaker's test feature vectors and speaker models has become a time-consuming process. This(More)
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