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Speaker Recognition System using Gaussian Mixture Model

by Athira Aroon, S.B. Dhonde
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 14
Year of Publication: 2015
Authors: Athira Aroon, S.B. Dhonde
10.5120/ijca2015907193

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 },
issue_date = { November 2015 },
volume = { 130 },
number = { 14 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 38-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number14/23281-2015907193/ },
doi = { 10.5120/ijca2015907193 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:25:36.302010+05:30
%A Athira Aroon
%A S.B. Dhonde
%T Speaker Recognition System using Gaussian Mixture Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 14
%P 38-40
%D 2015
%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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

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

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