NDLI: A Novel Reduction Method for Text-Independent Speaker Identification

Content Provider	IEEE Xplore Digital Library
Author	Yan Wang Xueyan Liu Yujuan Xing Ming Li
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. & Commun., LanZhou Univ. of Sci. Technol., Lanzhou (Yan Wang; Xueyan Liu; Yujuan Xing; Ming Li)
Abstract	SVM is a novel statistical learning method that has been successfully applied in speaker recognition. However, Extractive feature vectors from the speech are overlapped and noisy is included in the original data space, these problems can lead to experience difficulties, training complication during training SVM, and the result will be reduced during the recognition phase. In this paper, a novel method is proposed to reduce the noise and input vectors of the SVM. Firstly data dimensions are reduced and noise is removed by using PCA transform, secondly feature data are selected at boundary of each cluster as SVs by using Kernel-based fuzzy clustering technique. The training data, time and storage can be reduced remarkably compared with traditional SVM; the speaker identification system based on our proposed reduced support vector machine (RSVM) has better robustness compared with other reduced algorithms.
Starting Page	66
Ending Page	70
File Size	415312
Page Count	5
File Format	PDF
ISBN	9780769533049
DOI	10.1109/ICNC.2008.708
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Phase noise Reduced support vector machine (RSVM) Noise reduction Statistical learning Kernel-based fuzzy clustering Speaker recognition Data mining PCA Support vector machines Training data Speech recognition Feature extraction Speaker Identification Principal component analysis
Content Type	Text
Resource Type	Article

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