TEXT DEPENDENT SPEAKER RECOGNITION USING SHIFTED MFCC

被引：0
|
作者：
Mukherjee, Rishiraj [1 ]
Islam, Tanmoy [1 ]
Sankar, Ravi [1 ]
机构：
[1] Univ S Florida, Dept Elect Engn, Tampa, FL 33620 USA
来源：
2012 PROCEEDINGS OF IEEE SOUTHEASTCON | 2012年
关键词：
Gaussian Mixture Model (GMM); MFCC (Mel Frequency Cepstral Components); Speaker Recognition;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In the past decade, interest in using biometric technologies for person authentication in security systems has grown rapidly. Voice is one of the most promising and mature biometric modalities for secured access control. In this paper, we present a novel approach to recognize/identify speakers by including a new set of features and using Gaussian mixture models (GMMs). In this research, the concept of shifted MFCC is introduced so as to incorporate accent information in the recognition algorithm. The algorithm was evaluated using TIDIGIT dataset and the results showed improvements over the performance of our previous work [1].
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页数：4
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