In this paper we develop a simple mathematical model for reducing speaker recognition noise bias in the i-vector space. The method was successfully tested on two different databases covering distinct microphones and background noise scenarios. Substantial reduction in score variability was attained across distinct evaluation conditions which is particularly important in forensic applications. Although originally designed for addressing additive noise, we show that under certain circumstances the proposed method incidentally alleviates convolutive nuisance as well.
Cite as: Solewicz, Y., Aronowitz, H., Becker, T. (2016) Reducing Noise Bias in the i-Vector Space for Speaker Recognition. Proc. The Speaker and Language Recognition Workshop (Odyssey 2016), 372-376, doi: 10.21437/Odyssey.2016-54
@inproceedings{solewicz16_odyssey,
author={Yosef Solewicz and Hagai Aronowitz and Timo Becker},
title={{Reducing Noise Bias in the i-Vector Space for Speaker Recognition}},
year=2016,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2016)},
pages={372--376},
doi={10.21437/Odyssey.2016-54}
}