Abstract

In this paper, we propose a Lasso based framework to generate the sparse total variability supervectors (s-vectors). Rather than the factor analysis framework, which uses a low dimensional Eigenvoice subspace to represent the mean supervector, the proposed Lasso approach utilizes the l1 norm regularized least square estimation to project the mean supervector on a pre-defined dictionary. The number of samples in this dictionary is appreciably larger than the typical Eigenvoice rank but the l1 norm of the Lasso solution vector is constrained. Only a small number of samples in the dictionary are selected for representing the mean supervector, and most of the dictionary coefficients in the Lasso solution are 0. We denote these sparse dictionary coefficient vectors in the Lasso solutions as the s-vectors and model them using probabilistic linear discriminant analysis (PLDA) for speaker verification. The proposed …

Date

2012

Authors

Ming Li, Charley Lu, Anne Wang, Shrikanth Narayanan

Conference

Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference

Pages

1-4

Publisher

IEEE