During late-2013 through early-2014 NIST coordinated a special i-vector challenge based on data used in previous NIST Speaker Recognition Evaluations (SREs). Unlike evaluations in the SRE series, the i-vector challenge was run entirely online and used fixed-length feature vectors projected into a low-dimensional space (i-vectors) rather than audio recordings. These changes made the challenge more readily accessible, especially to participants from outside the audio processing field. Compared to the 2012 SRE, the i-vector challenge saw an increase in the number of participants by nearly a factor of two, and a two orders of magnitude increase in the number of systems submitted for evaluation. Initial results indicate the leading system achieved an approximate 37% improvement relative to the baseline system.
Bibliographic reference. Bansé, Désiré / Doddington, George R. / Garcia-Romero, Daniel / Godfrey, John J. / Greenberg, Craig S. / Martin, Alvin F. / McCree, Alan / Przybocki, Mark / Reynolds, Douglas A. (2014): "Summary and initial results of the 2013-2014 speaker recognition i-vector machine learning challenge", In INTERSPEECH-2014, 368-372.