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An Emotion Recognition System based on Right Truncated Gaussian Mixture Model

by N. Murali Krishna, Y. Srinivas, P. V. Lakshmi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 10
Year of Publication: 2012
Authors: N. Murali Krishna, Y. Srinivas, P. V. Lakshmi
10.5120/5733-7816

N. Murali Krishna, Y. Srinivas, P. V. Lakshmi . An Emotion Recognition System based on Right Truncated Gaussian Mixture Model. International Journal of Computer Applications. 42, 10 ( March 2012), 41-45. DOI=10.5120/5733-7816

@article{ 10.5120/5733-7816,
author = { N. Murali Krishna, Y. Srinivas, P. V. Lakshmi },
title = { An Emotion Recognition System based on Right Truncated Gaussian Mixture Model },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 10 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number10/5733-7816/ },
doi = { 10.5120/5733-7816 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:01.728589+05:30
%A N. Murali Krishna
%A Y. Srinivas
%A P. V. Lakshmi
%T An Emotion Recognition System based on Right Truncated Gaussian Mixture Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 10
%P 41-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This article address a novel emotion recognition system based on the Truncated Gaussian mixture model . The proposed system has been experimented over an gender independent emotion recognition database In the recent past, many models have been listed in the literature based on the emotion recognition, but these papers are more focused towards the speech, ignoring the emotion of the speaker at the time of speech which may be of significant importance at some particular instances such as BPO. To overcome this, we present a model using Right Truncated Gaussian mixture model and K-means algorithm to classify the emotion speeches. MFCC features of the emotions are extracted. The proposed system has been experimented over an gender independent emotion recognition database. The results obtained are evaluated using a confusion Matrix and compared with that of the Gaussian mixture model . Our model achieved a recognized rate of above 90 %.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Tgmm K-means Mfcc Feature Extraction Confusion Matrix

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