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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 topscoring Gaussians. In the present work we extend this method by using 2-dimensional indexing, which leads to simultaneous frame and Gaussian selection. Our results on the NIST 2002 speaker recognitiondoi:10.1109/icassp.2010.5495576 dblp:conf/icassp/SaeidiKMRF10 fatcat:xf2iukoib5aydppvmajs7ipz24