Common vector approach and its combination with GMM for text-independent speaker recognition

被引：9

作者：

Sadic, Selami ^{[1
]}

Gulmezoglu, M. Bilginer ^{[2
]}

机构：

[1] 1st Air Supply & Maintenance Ctr, Dept Technol & Weapon Syst Dev, TR-26320 Eskisehir, Turkey

[2] Eskisehir Osmangazi Univ, Dept Elect & Elect Engn, Eskisehir, Turkey

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 09期

关键词：

Speaker recognition; Gaussian mixture models; Fisher's linear discriminant analysis; Common vector approach; MAXIMUM-LIKELIHOOD;

D O I：

10.1016/j.eswa.2011.03.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the common vector approach (CVA) is newly used for text-independent speaker recognition. The performance of CVA is compared with those of Fisher's linear discriminant analysis (FLDA) and Gaussian mixture models (GMM). The recognition rates obtained for the TIMIT database indicate that CVA and GMM are superior to FLDA. However, while the recognition rates obtained from CVA and GMM are identical, CVA enjoys advantages in terms of processing power and memory requirement. In order to obtain better results than those achieved with GMM, a new method which is a combination of CVA and GMM is proposed in this paper. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：11394 / 11400

页数：7

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