Speech emotion detection using supervised, unsupervised and feature selection algorithms

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.

Bibliographic Details
Main Authors:	Rifat, Abu Nuraiya Mahfuza Yesmin, Biswas, Aditi, Chowdhury, Nadia Farhin
Other Authors:	Uddin, Jia
Format:	Thesis
Language:	English
Published:	BRAC University 2019
Subjects:	SER MFCC Random forest Gradient boosting SVM CNN RFE P-Value PCA Supervised learning (Machine learning)
Online Access:	http://hdl.handle.net/10361/12287

id	10361-12287
record_format	dspace
spelling	10361-122872022-01-26T10:18:14Z Speech emotion detection using supervised, unsupervised and feature selection algorithms Rifat, Abu Nuraiya Mahfuza Yesmin Biswas, Aditi Chowdhury, Nadia Farhin Uddin, Jia Department of Computer Science and Engineering, Brac University SER MFCC Random forest Gradient boosting SVM CNN RFE P-Value PCA Supervised learning (Machine learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 38-40). A tremendous research is being done on Speech Emotion Recognition (SER) in the recent years with its main motto to improve human machine interaction. In this thesis work,we have introduced a scheme for emotion recognition from speech. We have classi ed three emotions (happy, angry and sad) for both male and female. Recognition task has been done using Mel-frequency Cepstrum Coe cient (MFCC) based features.Four classi ers are used for the purpose of classi cation. They are Random Forest, Gradient Boosting, SVMand CNN. Among them, CNN has shown the best accuracy of 71.17%. Random Forest has shown an accuracy of 61.26%, Gradient Boosting 60.36% and SVM60 36%. After using RFE method, PCA and P-Valuefor less signi cant feature reduction the accuracy improved to 62.16% for Random Forest, 62.16% for Gradient Boostingand 61.26% for SVM. Abu Nuraiya Mahfuza Yesmin Rifat Aditi Biswas Nadia Farhin Chowdhury B. Computer Science and Engineering 2019-07-02T04:06:41Z 2019-07-02T04:06:41Z 2019 2019-04 Thesis ID 15101048 ID 16301135 ID 15301087 http://hdl.handle.net/10361/12287 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 40 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	SER MFCC Random forest Gradient boosting SVM CNN RFE P-Value PCA Supervised learning (Machine learning)
spellingShingle	SER MFCC Random forest Gradient boosting SVM CNN RFE P-Value PCA Supervised learning (Machine learning) Rifat, Abu Nuraiya Mahfuza Yesmin Biswas, Aditi Chowdhury, Nadia Farhin Speech emotion detection using supervised, unsupervised and feature selection algorithms
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.
author2	Uddin, Jia
author_facet	Uddin, Jia Rifat, Abu Nuraiya Mahfuza Yesmin Biswas, Aditi Chowdhury, Nadia Farhin
format	Thesis
author	Rifat, Abu Nuraiya Mahfuza Yesmin Biswas, Aditi Chowdhury, Nadia Farhin
author_sort	Rifat, Abu Nuraiya Mahfuza Yesmin
title	Speech emotion detection using supervised, unsupervised and feature selection algorithms
title_short	Speech emotion detection using supervised, unsupervised and feature selection algorithms
title_full	Speech emotion detection using supervised, unsupervised and feature selection algorithms
title_fullStr	Speech emotion detection using supervised, unsupervised and feature selection algorithms
title_full_unstemmed	Speech emotion detection using supervised, unsupervised and feature selection algorithms
title_sort	speech emotion detection using supervised, unsupervised and feature selection algorithms
publisher	BRAC University
publishDate	2019
url	http://hdl.handle.net/10361/12287
work_keys_str_mv	AT rifatabunuraiyamahfuzayesmin speechemotiondetectionusingsupervisedunsupervisedandfeatureselectionalgorithms AT biswasaditi speechemotiondetectionusingsupervisedunsupervisedandfeatureselectionalgorithms AT chowdhurynadiafarhin speechemotiondetectionusingsupervisedunsupervisedandfeatureselectionalgorithms
_version_	1814308680258027520

Speech emotion detection using supervised, unsupervised and feature selection algorithms

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