In this paper we describe the most recent MIT Lincoln Laboratory language recognition system developed for the NIST 2015 Language Recognition Evaluation (LRE). The submission features a fusion of five core classifiers, with most systems developed in the context of an i-vector framework. The 2015 evaluation presented new paradigms. First, the evaluation included fixed training and open training tracks for the first time; second, language classification performance was measured across 6 language clusters using 20 language classes instead of an N-way language task; and third, performance was measured across a nominal 3-30 second range. Results are presented for the average performance across the 6 language clusters for both the fixed and open training tasks. On the 6-cluster metric the Lincoln system achieved average costs of 0.173 and 0.168 for the fixed and open tasks respectively.
Cite as: Torres-Carrasquillo, P., Dehak, N., Godoy, E., Reynolds, D., Richardson, F., Shum, S., Singer, E., Sturim, D. (2016) The MITLL NIST LRE 2015 Language Recognition System. Proc. The Speaker and Language Recognition Workshop (Odyssey 2016), 196-203, doi: 10.21437/Odyssey.2016-28
@inproceedings{torrescarrasquillo16_odyssey,
author={Pedro Torres-Carrasquillo and Najim Dehak and Elizabeth Godoy and Douglas Reynolds and Fred Richardson and Stephen Shum and Elliot Singer and Douglas Sturim},
title={{The MITLL NIST LRE 2015 Language Recognition System}},
year=2016,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2016)},
pages={196--203},
doi={10.21437/Odyssey.2016-28}
}