  This article describes the systems jointly submitted by Institute for
Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Universit\'e du Maine
(LIUM), Nanyang Technology University (NTU) and the University of Eastern
Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE). The
submitted system is a fusion of nine sub-systems based on i-vectors extracted
from different types of features. Given the i-vectors, several classifiers are
adopted for the language detection task including support vector machines
(SVM), multi-class logistic regression (MCLR), Probabilistic Linear
Discriminant Analysis (PLDA) and Deep Neural Networks (DNN).
