Speaker Verification Based on TES-PCA Classifier and SVM plus FCM Clustering

被引:0
作者
Xing Yujuan [1 ]
Tan Ping [1 ]
Zhang Chengwen [1 ]
机构
[1] Lanzhou Univ Arts & Sci, Sch Digital Media, Lanzhou 730000, Peoples R China
来源
BIOMETRIC RECOGNITION, CCBR 2015 | 2015年 / 9428卷
关键词
Speaker recognition; Support vector machine; Principal component analysis; Truncation error space classifier; MODEL;
D O I
10.1007/978-3-319-25417-3_55
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Speaker verification is an important branch of speaker recognition. In this paper, a novel hierarchical speaker verification method based on TES-PCA Classifier and support vector machine plus Fuzzy c-means clustering was proposed for the sake of improving performance of speaker verification. In this algorithm, we utilized PCA and Fuzzy c-means clustering to select more discriminant and lower dimensional feature vectors firstly. And then, the truncation error space(TES) was obtained from PCA transformation matrix. The R target speakers were selected fleetly from TES-PCA classifier. Finally, support vector machine was used to make final decision. The experimental results showed that our proposed method could improve recognition accuracy remarkably and the system has better robustness compared with traditional methods.
引用
收藏
页码:464 / 471
页数:8
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