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SVM Scheme for Speech Emotion Recognition using MFCC Feature

by A.milton, S. Sharmy Roy, S. Tamil Selvi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 9
Year of Publication: 2013
Authors: A.milton, S. Sharmy Roy, S. Tamil Selvi
10.5120/11872-7667

A.milton, S. Sharmy Roy, S. Tamil Selvi . SVM Scheme for Speech Emotion Recognition using MFCC Feature. International Journal of Computer Applications. 69, 9 ( May 2013), 34-39. DOI=10.5120/11872-7667

@article{ 10.5120/11872-7667,
author = { A.milton, S. Sharmy Roy, S. Tamil Selvi },
title = { SVM Scheme for Speech Emotion Recognition using MFCC Feature },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 9 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number9/11872-7667/ },
doi = { 10.5120/11872-7667 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:29:49.153200+05:30
%A A.milton
%A S. Sharmy Roy
%A S. Tamil Selvi
%T SVM Scheme for Speech Emotion Recognition using MFCC Feature
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 9
%P 34-39
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Emotion recognition from speech has developed as a recent research area in Human–Computer Interaction. The objective of this paper is to use a 3-stage Support Vector Machine classifier to classify seven different emotions present in the Berlin Emotional Database. For the purpose of classification, MFCC features from all the 535 files present in the database are extracted. Nine statistical measurements are performed over these features from each frame of a sentence. The linear and RBF kernels are employed in hierarchical SVM with RBF sigma value equal to one. For training and testing of data, 10-fold cross-validation is used. Performance analysis is done by using the confusion matrix and the accuracy obtained is 68%.

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Index Terms

Computer Science
Information Sciences

Keywords

Speech Emotion Recognition MFCC SVM RBF Linear Kernel

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