In this paper, the sparse representation computed by l1- minimization with quadratic constraints is employed to model the i-vectors in the low dimensional total variability space after performing the Within-Class Covariance Normalization and Linear Discriminate Analysis channel compensation. First, we propose the background normalized l2 residual as a scoring criterion. Second, we demonstrate that the Tnorm can be efficiently achieved by using the Tnorm data as the non-target samples in the over-complete dictionary. Finally, by fusing with the conventional i-vector based support vector machine (SVM) and cosine distance scoring system, we demonstrate overall system performance improvement. Exper- imental results show that the proposed fusion system achieved 4.05% (male) and 5.25% (female) equal error rate (EER) after Tnorm on the single-single multi-language handheld telephone task of NIST SRE 2008 and outperformed the SVM baseline by yielding 7.1% and 4.9% relative EER reduction for the male and female tasks, respectively.
Bibliographic reference. Li, Ming / Zhang, Xiang / Yan, Yonghong / Narayanan, Shrikanth (2011): "Speaker verification using sparse representations on total variability i-vectors", In INTERSPEECH-2011, 2729-2732.