Automatic language identification using SVM-UBM

As powerful theoretical and computational tools,Support Vector Machines(SVMs) have been widely used in pattern classification of many areas.In this paper,we present a general framework for language identification using SVMs,introduce the use of Louradour sequence kernel into language identification system,and develop a universal background Gaussian Mixture Model(GMM) to improve it's performance.Experiment results demonstrate that the SVM-UBM system not only yields performance superior to those of a GMM classifier but also outperforms the system using Generalized Linear Discriminant Sequence(GLDS) kernel.