关键词: 语言辨识, 支持向量机, 序列核, 高斯混合模型, 全局背景模型

Abstract: 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 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.

Key words: language identification, support vector machine, sequence kernel, gaussian mixture model, universal background model