Speaker identification based on EMD

Author

Liu, Yali ; Yang, Hongwu ; Zhou, Hui

Author_Institution

Coll. of Phys. & Electron. Eng., Northwest Normal Univ., Lanzhou, China

fYear

2009

fDate

6-8 Nov. 2009

Firstpage

808

Lastpage

812

Abstract

This paper proposes a novel approach which combines empirical mode decomposition (EMD), short-time analysis and support vector machine (SVM) for text-independent speaker recognition. Short-time analysis is used for the result of empirical mode decomposition to extract speech features of speakers, and then the support vector machine are used for speaker recognition. Experiments demonstrate that the proposed approach outperforms GMM based traditional methods, with the increased recognition rate from 92.5% to 95.1%.

Keywords

feature extraction; speaker recognition; support vector machines; SVM; empirical mode decomposition; feature extraction; short-time analysis; speaker identification; support vector machine; text-independent speaker recognition; Cepstrum; Data mining; Educational institutions; Feature extraction; Information analysis; Mel frequency cepstral coefficient; Signal analysis; Speaker recognition; Speech analysis; Support vector machines; empirical mode decomposition (EMD); short-time analysis; speaker recognition; support vector machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Network Infrastructure and Digital Content, 2009. IC-NIDC 2009. IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4898-2

Electronic_ISBN

978-1-4244-4900-6

Type

conf

DOI

10.1109/ICNIDC.2009.5360889

Filename

5360889