Title :
Speaker identification based on EMD
Author :
Liu, Yali ; Yang, Hongwu ; Zhou, Hui
Author_Institution :
Coll. of Phys. & Electron. Eng., Northwest Normal Univ., Lanzhou, China
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);
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
DOI :
10.1109/ICNIDC.2009.5360889
