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 independent distributions to be used for the two classes. 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.
Cite as: Cumani, S., Laface, P. (2014) Generative pairwise models for speaker recognition. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 273-279, doi: 10.21437/Odyssey.2014-41
@inproceedings{cumani14_odyssey,
author={Sandro Cumani and Pietro Laface},
title={{Generative pairwise models for speaker recognition}},
year=2014,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},
pages={273--279},
doi={10.21437/Odyssey.2014-41}
}