Speaker identification based on Multi-Reduced SVM

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	Speaker identification based on Multi-Reduced SVM
	Li, Ming; Liu, Xueyan; Wu, Fuwen
	2007
页码	371-+
英文摘要	SVM is a novel type of statistical learning methods that has been successfully used in speaker recognition. However, Training SVM consumes long computing time and large memory with all training data. This paper proposes a speaker identification method based on Multi-Reduced support vector machine (MRSVM). MRSVM has two reduction steps. Firstly, speech feature dimensions are reduced by using KL transform, the noise is removed from speech simultaneity. Secondly, speech feature data are selected at boundary of each cluster as SVs by using Kernel-based fuzzy clustering technique. Experiment results show that not only the training data, training time and storage can be reduced remarkably, but also the identification accuracy can be improved by the proposed MRSVM compared with other reduced algorithms and the system has better robustness.
会议录	FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 3, PROCEEDINGS
会议录出版者	IEEE COMPUTER SOC
会议录出版地	10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
语种	英语
资助项目	Foundation of Science Research of Gansu Education Office[0603-10]
WOS研究方向	Computer Science ; Engineering
WOS记录号	WOS:000252460600074
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/38085]
专题	兰州理工大学
通讯作者	Li, Ming
作者单位	Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Li, Ming,Liu, Xueyan,Wu, Fuwen. Speaker identification based on Multi-Reduced SVM[C]. 见:.