The problem of impostor dataset selection for GMM-based speaker verification is addressed through the recently proposed data-driven background dataset refinement technique. The SVM-based refinement technique selects from a candidate impostor dataset those examples that are most frequently selected as support vectors when training a set of SVMs on a development corpus. This study demonstrates the versatility of dataset refinement in the task of selecting suitable impostor datasets for use in GMM-based speaker verification. The use of refined Z- and T-norm datasets provided performance gains of 15% in EER in the NIST 2006 SRE over the use of heuristically selected datasets. The refined datasets were shown to generalise well to the unseen data of the NIST 2008 SRE.
Bibliographic reference. McLaren, Mitchell / Vogt, Robbie / Baker, Brendan / Sridharan, Sridha (2009): "Improved GMM-based speaker verification using SVM-driven impostor dataset selection", In INTERSPEECH-2009, 1267-1270.