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Reseach Article

Emotion Recognition from Speech using Discriminative Features

by Purnima Chandrasekar, Santosh Chapaneri, Deepak Jayaswal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 16
Year of Publication: 2014
Authors: Purnima Chandrasekar, Santosh Chapaneri, Deepak Jayaswal
10.5120/17775-8913

Purnima Chandrasekar, Santosh Chapaneri, Deepak Jayaswal . Emotion Recognition from Speech using Discriminative Features. International Journal of Computer Applications. 101, 16 ( September 2014), 31-36. DOI=10.5120/17775-8913

@article{ 10.5120/17775-8913,
author = { Purnima Chandrasekar, Santosh Chapaneri, Deepak Jayaswal },
title = { Emotion Recognition from Speech using Discriminative Features },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 16 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number16/17775-8913/ },
doi = { 10.5120/17775-8913 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:31:51.721435+05:30
%A Purnima Chandrasekar
%A Santosh Chapaneri
%A Deepak Jayaswal
%T Emotion Recognition from Speech using Discriminative Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 16
%P 31-36
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Creating an accurate Speech Emotion Recognition (SER) system depends on extracting features relevant to that of emotions from speech. In this paper, the features that are extracted from the speech samples include Mel Frequency Cepstral Coefficients (MFCC), energy, pitch, spectral flux, spectral roll-off and spectral stationarity. In order to avoid the 'curse of dimensionality', statistical parameters, i. e. mean, variance, median, maximum, minimum, and index of dispersion have been applied on the extracted features. For classifying the emotion in an unknown test sample, Support Vector Machines (SVM) has been chosen due to its proven efficiency. Through experimentation on the chosen features, an average classification accuracy of 86. 6% has been achieved using one-v/s-all multi-class SVM which is further improved to 100% when reduced to binary form problem. Classifier metrics viz. precision, recall, and F-score values show that the proposed system gives improved accuracy for Emo-DB.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature extraction dimensionality reduction feature classification Support Vector Machines Emotion recognition