ISCA Archive Interspeech 2014
ISCA Archive Interspeech 2014

Summary and initial results of the 2013-2014 speaker recognition i-vector machine learning challenge

Désiré Bansé, George R. Doddington, Daniel Garcia-Romero, John J. Godfrey, Craig S. Greenberg, Alvin F. Martin, Alan McCree, Mark Przybocki, Douglas A. Reynolds

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.


doi: 10.21437/Interspeech.2014-86

Cite as: Bansé, D., Doddington, G.R., Garcia-Romero, D., Godfrey, J.J., Greenberg, C.S., Martin, A.F., McCree, A., Przybocki, M., Reynolds, D.A. (2014) Summary and initial results of the 2013-2014 speaker recognition i-vector machine learning challenge. Proc. Interspeech 2014, 368-372, doi: 10.21437/Interspeech.2014-86

@inproceedings{banse14_interspeech,
  author={Désiré Bansé and George R. Doddington and Daniel Garcia-Romero and John J. Godfrey and Craig S. Greenberg and Alvin F. Martin and Alan McCree and Mark Przybocki and Douglas A. Reynolds},
  title={{Summary and initial results of the 2013-2014 speaker recognition i-vector machine learning challenge}},
  year=2014,
  booktitle={Proc. Interspeech 2014},
  pages={368--372},
  doi={10.21437/Interspeech.2014-86}
}