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Reseach Article

Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Distant Microphone Speech

by K.Jyothi, Dr.V Sailaja, Dr.K. Srinivasa Rao
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 14 - Number 4
Year of Publication: 2011
Authors: K.Jyothi, Dr.V Sailaja, Dr.K. Srinivasa Rao
10.5120/1835-2463

K.Jyothi, Dr.V Sailaja, Dr.K. Srinivasa Rao . Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Distant Microphone Speech. International Journal of Computer Applications. 14, 4 ( January 2011), 5-9. DOI=10.5120/1835-2463

@article{ 10.5120/1835-2463,
author = { K.Jyothi, Dr.V Sailaja, Dr.K. Srinivasa Rao },
title = { Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Distant Microphone Speech },
journal = { International Journal of Computer Applications },
issue_date = { January 2011 },
volume = { 14 },
number = { 4 },
month = { January },
year = { 2011 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume14/number4/1835-2463/ },
doi = { 10.5120/1835-2463 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:02:59.589511+05:30
%A K.Jyothi
%A Dr.V Sailaja
%A Dr.K. Srinivasa Rao
%T Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Distant Microphone Speech
%J International Journal of Computer Applications
%@ 0975-8887
%V 14
%N 4
%P 5-9
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An effective and efficient speaker Identification (SI) system requires a robust feature extraction module followed by a speaker modeling scheme for generalized representation of these features. In recent, years Speaker Identification has seen significant advancement, but improvements have tended to be bench marked on the near field speech, ignoring the more realistic setting of far field instrumented speaker. A novel speaker model is developed by using Finite Multivariate Generalized Gaussian Mixture Model, Minimum Variance Distortion less Response Cepstral coefficients as feature Vectors. The performance of the developed model is studied through experimental evaluation with 45 speaker’s data base and identification accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Generalized Gaussian Mixture Model Minimum Variance Distortion less Response Cepstral coefficients EM algorithm