A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
McLaren, M., Vogt, R., Baker, B., & Sridharan, S. (2009). Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5558 LNCS, pp. 474–483). https://doi.org/10.1007/978-3-642-01793-3_49
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