Research Article

Speaker Recognition System using Gaussian Mixture Model

by  Athira Aroon, S.B. Dhonde
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Issue 14
Published: November 2015
Authors: Athira Aroon, S.B. Dhonde
10.5120/ijca2015907193
PDF

Athira Aroon, S.B. Dhonde . Speaker Recognition System using Gaussian Mixture Model. International Journal of Computer Applications. 130, 14 (November 2015), 38-40. DOI=10.5120/ijca2015907193

                        @article{ 10.5120/ijca2015907193,
                        author  = { Athira Aroon,S.B. Dhonde },
                        title   = { Speaker Recognition System using Gaussian Mixture Model },
                        journal = { International Journal of Computer Applications },
                        year    = { 2015 },
                        volume  = { 130 },
                        number  = { 14 },
                        pages   = { 38-40 },
                        doi     = { 10.5120/ijca2015907193 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2015
                        %A Athira Aroon
                        %A S.B. Dhonde
                        %T Speaker Recognition System using Gaussian Mixture Model%T 
                        %J International Journal of Computer Applications
                        %V 130
                        %N 14
                        %P 38-40
                        %R 10.5120/ijca2015907193
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper,features for text-independent speaker recognition has been evaluated. Speaker identification from a set of templates and analyzing speaker recognition rate by extracting several key features like Mel Frequency Cepstral Coefficients [MFCC] from the speech signals of those persons by using the process of feature extraction using MATLAB2013 .These features are effectively captured using feature matching technique like Gaussian Mixture Model [GMM] , with varying mixture components of mixture model and the analyzing its effect on recognition rate . Improve the speaker recognition rate by varying the input parameters of the classifier. The experiments are evaluated on TIMIT Database effectively for a speech signal sampled at 16kHz.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Gaussian Mixture Model [GMM] Mel Frequency Cepstral Coefficients [MFCC] Speaker Recognition rate.

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