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Enhanced HMM Speech Emotion Recognition using SVM and Neural Classifier

by Preeti Suri, Bhupinder Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 12
Year of Publication: 2014
Authors: Preeti Suri, Bhupinder Singh
10.5120/15260-3914

Preeti Suri, Bhupinder Singh . Enhanced HMM Speech Emotion Recognition using SVM and Neural Classifier. International Journal of Computer Applications. 87, 12 ( February 2014), 17-20. DOI=10.5120/15260-3914

@article{ 10.5120/15260-3914,
author = { Preeti Suri, Bhupinder Singh },
title = { Enhanced HMM Speech Emotion Recognition using SVM and Neural Classifier },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 12 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 17-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number12/15260-3914/ },
doi = { 10.5120/15260-3914 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:44.035190+05:30
%A Preeti Suri
%A Bhupinder Singh
%T Enhanced HMM Speech Emotion Recognition using SVM and Neural Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 12
%P 17-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Emotion classification has been a research area for decades. Identifying the correct emotion of the user helps in a lot of areas like crime investigation and other related research works. This particular thesis work has been done using 4 emotion categories in which SVM and Custom Neural Network has been used as a classifier. This thesis work is consisted of two main sections. The first section is called the training part and the second part is called the testing part. In the training part we have used different voice files of different categories like happy, sad, angry and aggressive to train the system according to the classified properties of the speech samples. To train the system feed forward method has been used and the database format is . mat. In the testing section we have used SVM to binarize the features of the input sample and Neural to finally classify the entire architecture. The custom neural network in this research work has been provided two categories of sample, the first sample is the training data set and the final sample is the testing data set. The finally accuracy of the plot comes out to be more than 90 %

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Index Terms

Computer Science
Information Sciences

Keywords

Emotion States Energy Pitch Emotion Classifier

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