This paper proposes a simple model for speaker recognition
based on i–vector pairs, and analyzes its similarity and differences
with respect to the state–of–the–art Probabilistic Linear
Discriminant Analysis (PLDA) and Pairwise Support Vector
Machine (PSVM) models. Similar to the discriminative PSVM
approach, we propose a generative model of i–vector pairs,
rather than an usual i–vector based model. The model is based
on two Gaussian distributions, one for the “same speakers” and
the other for the “different speakers” i–vector pairs, and on the
assumption that the i–vector pairs are independent. This independence assumption allows the distributions of the two classes to be independently estimated. The “Two–Gaussian” approach can be extended to the Heavy–Tailed distributions, still allowing a fast closed form solution to be obtained for testing i–vector pairs.
We show that this model is closely related to PLDA and to PSVM models, and that tested on the female part of the tel–tel NIST SRE 2010 extended evaluation set, it is able to achieve comparable accuracy with respect to the other models, trained with different objective functions and training procedures