During late-2013 through mid-2014 NIST coordinated a special machine learning challenge based on the i-vector paradigm widely used by state-of-the-art speaker recognition systems. The i-vector challenge was run entirely online and used as source data fixed-length feature vectors projected into a low-dimensional space (i-vectors) rather than audio recordings. These changes made the challenge more readily accessible, enabled system comparison with consistency in the front-end and in the amount and type of training data, and facilitated exploration of many more approaches than would be possible in a single evaluation as traditionally run by NIST. Compared to the 2012 NIST Speaker Recognition Evaluation, the i-vector challenge saw approximately twice as many participants, and a nearly two orders of magnitude increase in the number of systems submitted for evaluation. Initial results indicate that the leading system achieved a relative improvement of approximately 38% over the baseline system.
Cite as: Mccree, A., Reynolds, D., Garcia-Romero, D., Kinnunen, T., Greenberg, C., Bansé, D., Doddington, G., Godfrey, J., Martin, A., Przybocki, M. (2014) The NIST 2014 Speaker Recognition i-vector Machine Learning Challenge. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 224-230, doi: 10.21437/Odyssey.2014-34
@inproceedings{mccree14b_odyssey,
author={Alan Mccree and Douglas Reynolds and Daniel Garcia-Romero and Tomi Kinnunen and Craig Greenberg and Désiré Bansé and George Doddington and John Godfrey and Alvin Martin and Mark Przybocki},
title={{The NIST 2014 Speaker Recognition i-vector Machine Learning Challenge}},
year=2014,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},
pages={224--230},
doi={10.21437/Odyssey.2014-34}
}