|Pedro Torres-Carrasquillo, Najim Dehak, Elizabeth Godoy, Douglas Reynolds, Fred Richardson, Stephen Shum, Elliot Singer, Douglas Sturim
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.