This paper studies the scenario of migrating from one i-vector-based speaker recognition system (SRE) to another, i.e. comparing the i-vectors produced by one system with those produced by another system. System migration would typically be motivated by deploying a system with improved recognition accuracy, e.g. because of technological upgrade, or because of the necessity of processing new kind of data, etc. Unfortunately, such migration is very likely to result in the incompatibility between the new and the original i-vectors and, therefore, in the inability of comparing the two. This work studies various topologies of Regression Neural Networks for transforming i-vectors from three different systems so that — with slight loss in the accuracy — they are compatible with the reference system. We present the results on the NIST SRE 2010 telephone condition.
Cite as: Glembek, O., Matějka, P., Plchot, O., Pešán, J., Burget, L., Schwarz, P. (2015) Migrating i-vectors between speaker recognition systems using regression neural networks. Proc. Interspeech 2015, 2327-2331, doi: 10.21437/Interspeech.2015-504
@inproceedings{glembek15_interspeech,
author={Ondřej Glembek and Pavel Matějka and Oldřich Plchot and Jan Pešán and Lukáš Burget and Petr Schwarz},
title={{Migrating i-vectors between speaker recognition systems using regression neural networks}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={2327--2331},
doi={10.21437/Interspeech.2015-504}
}