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).
['Kong-Aik Lee', 'Ville Hautamäki', 'Anthony Larcher', 'Wei Rao', 'Hanwu Sun', 'Trung Hieu Nguyen', 'Guangsen Wang', 'Aleksandr Sizov', 'Ivan Kukanov', 'Amir Hossein Poorjam', 'Trung Ngo Trong', 'Xiong Xiao', 'Chenglin Xu', 'Haihua Xu', 'Bin Ma', 'Haizhou Li', 'Sylvain Meignier']
Fantastic 4 system for NIST 2015 Language Recognition Evaluation