Fantastic 4 system for NIST 2015 Language Recognition Evaluation
This work addresses language detection for evaluation benchmarks, but it is incremental as it builds on existing i-vector and classifier methods.
The authors tackled the problem of language recognition by developing a fusion system that combines nine sub-systems using i-vectors and various classifiers, achieving competitive results in the NIST 2015 Language Recognition Evaluation.
This article describes the systems jointly submitted by Institute for Infocomm (I$^2$R), the Laboratoire d'Informatique de l'Université 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).