Generative pairwise models for speaker recognition
Sandro Cumani, Pietro Laface · 2014
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