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In this paper, we propose a set of criteria for the selection of the most relevant frames in order to improve text-independent speaker automatic recognition (TISAR) task. The selection is carried out on the short term Cepstral feature vectors such as PLP and MFCC and performed at the front end processing level. The proposed criteria mainly attempt to select vectors lying far from the universal background model (UBM). Experiments are conducted on the MOBIO database and show that the selectiondoi:10.5220/0006392100510057 dblp:conf/sigmap/RouiguebNT17 fatcat:paisdgooynde3pzxolvrowavf4