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基于SVM-UBM的语言辨识系统

Automatic language identification using SVM-UBM
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摘要 支持向量机作为强大的理论和计算工具,已成功地应用在模式识别的众多领域中。研究了将支持向量机模型(SVM)应用于语言辨识的理论框架,提出了将Louradour序列核应用于语言辨识,并利用高斯混合模型(GMM)构造全局背景模型(UBM)对其进行了改进,从而导出了基于SVM-UBM的语言辨识系统。相关实验结果表明,该系统的识别率高于经典的高斯混合模型(GMM)和基于广义线性区分性核(GLDS)的支持向量机模型。 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.
作者 张文林 李弼程 屈丹
机构地区 信息工程大学信息工程学院
出处 《计算机工程与应用》 CSCD 北大核心 2007年第10期41-43,共3页 Computer Engineering and Applications
基金 国家自然科学基金(the National Natural Science Foundation of China under Grant No.60372038)
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 language identification support vector machine sequence kernel Gaussian mixture model universal background model
分类号 TN912.3 [电子电信—通信与信息系统]
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参考文献15

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二级参考文献6

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计算机工程与应用
2007年 第10期

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