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A data-driven background dataset refinement technique was\udrecently proposed for SVM based speaker verification. This method selects\uda refined SVM background dataset from a set of candidate impostor\udexamples after individually ranking examples by their relevance. This\udpaper extends this technique to the refinement of the T-norm dataset\udfor SVM-based speaker verification. The independent refinement of the\udbackground and T-norm datasets provides a means of investigating the\udsensitivity of SVM-based speaker verification performance to the selection\udof each of these datasets. Using refined datasets provided improvements\udof 13% in min. DCF and 9% in EER over the full set of impostor\udexamples on the 2006 SRE corpus with the majority of these gains due\udto refinement of the T-norm dataset. Similar trends were observed for\udthe unseen data of the NIST 2008 SRE

Data-Driven Impostor Selection for T-Norm Score Normalisation and the Background Dataset in SVM-Based Speaker Verification