Summary of the 2015 NIST Language Recognition i-Vector Machine Learning Challenge

Audrey Tong, Craig Greenberg, Alvin Martin, Desire Banse, John Howard, Hui Zhao, George Doddington, Daniel Garcia-Romero, Alan McCree, Douglas Reynolds, Elliot Singer, Jaime Hernandez-Cordero, Lisa Mason


In 2015 NIST coordinated the first language recognition evaluation (LRE) that used i-vectors as input, with the goals of attracting researchers outside of the speech processing community to tackle the language recognition problem, exploring new ideas in machine learning for use in language recognition, and improving recognition accuracy. The Language Recognition i-Vector Machine Learning Challenge, taking place over a period of four months, was well-received with 56 participants from 44 unique sites and over 3700 submissions, surpassing the participation levels of all previous traditional track LREs. The results of 46 of the 56 participants were better than the provided baseline system, with the best system achieving approximately 55% relative improvement over the baseline.


DOI: 10.21437/Odyssey.2016-43

Cite as

Tong, A., Greenberg, C., Martin, A., Banse, D., Howard, J., Zhao, H., Doddington, G., Garcia-Romero, D., McCree, A., Reynolds, D., Singer, E., Hernandez-Cordero, J., Mason, L. (2016) Summary of the 2015 NIST Language Recognition i-Vector Machine Learning Challenge. Proc. Odyssey 2016, 297-302.

Bibtex
@inproceedings{Tong+2016,
author={Audrey Tong and Craig Greenberg and Alvin Martin and Desire Banse and John Howard and Hui Zhao and George Doddington and Daniel Garcia-Romero and Alan McCree and Douglas Reynolds and Elliot Singer and Jaime Hernandez-Cordero and Lisa Mason},
title={Summary of the 2015 NIST Language Recognition i-Vector Machine Learning Challenge},
year=2016,
booktitle={Odyssey 2016},
doi={10.21437/Odyssey.2016-43},
url={http://dx.doi.org/10.21437/Odyssey.2016-43},
pages={297--302}
}