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Text-Independent Speaker Recognition using Emotional Features and Generalized Gamma Distribution

by K Suri Babu, Srinivas Yarramalle, Suresh Varma Penumatsa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 2
Year of Publication: 2012
Authors: K Suri Babu, Srinivas Yarramalle, Suresh Varma Penumatsa
10.5120/6880-9183

K Suri Babu, Srinivas Yarramalle, Suresh Varma Penumatsa . Text-Independent Speaker Recognition using Emotional Features and Generalized Gamma Distribution. International Journal of Computer Applications. 46, 2 ( May 2012), 24-26. DOI=10.5120/6880-9183

@article{ 10.5120/6880-9183,
author = { K Suri Babu, Srinivas Yarramalle, Suresh Varma Penumatsa },
title = { Text-Independent Speaker Recognition using Emotional Features and Generalized Gamma Distribution },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 2 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 24-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number2/6880-9183/ },
doi = { 10.5120/6880-9183 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:38:43.035976+05:30
%A K Suri Babu
%A Srinivas Yarramalle
%A Suresh Varma Penumatsa
%T Text-Independent Speaker Recognition using Emotional Features and Generalized Gamma Distribution
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 2
%P 24-26
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this article, a novel methodology of text independent speaker recognition associated with emotional features is proposed. MFCC and LPC features are considered for speaker voice feature extraction. The speaker data is classified using generalized gamma distribution. The experiment is conducted on the emotional speech data base, containing 50 speakers with 5 different emotions namely happy, angry, sad, boredom and neutral. This approach is very much useful in costumer care and call centre applications. The accuracy of the developed model is presented using Confusion matrix. The results show that there is a great influence of the emotion state, while identifying the speaker.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Text-independent Speaker Recognition Generalized Gamma Distribution Mfcc Lpc Emotions Confusion Matrix

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