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Speech emotion classification using SVM and MLP on prosodic and voice quality features

In this paper, a comparison of emotion classification undertaken by the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP) Neural Network, using prosodic and voice quality features extracted from the Berlin Emotional Database, is reported. The features were extracted using PRAAT tools...

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Main Authors: Idris, Inshirah, Salam, Md. Sah, Sunar, Mohd. Shahrizal
Format: Article
Language:English
Published: Penerbit UTM Press 2016
Subjects:
QA76 Computer software
Online Access:http://eprints.utm.my/id/eprint/71237/1/MdSahSalam2016_SpeechemotionclassificationusingSVM.pdf
http://eprints.utm.my/id/eprint/71237/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960154852&doi=10.11113%2fjt.v78.6925&partnerID=40&md5=077bb5e73345f665103c0fb5d9df3473
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spelling my.utm.712372017-11-15T04:31:12Z http://eprints.utm.my/id/eprint/71237/ Speech emotion classification using SVM and MLP on prosodic and voice quality features Idris, Inshirah Salam, Md. Sah Sunar, Mohd. Shahrizal QA76 Computer software In this paper, a comparison of emotion classification undertaken by the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP) Neural Network, using prosodic and voice quality features extracted from the Berlin Emotional Database, is reported. The features were extracted using PRAAT tools, while the WEKA tool was used for classification. Different parameters were set up for both SVM and MLP, which are used to obtain an optimized emotion classification. The results show that MLP overcomes SVM in overall emotion classification performance. Nevertheless, the training for SVM was much faster when compared to MLP. The overall accuracy was 76.82% for SVM and 78.69% for MLP. Sadness was the emotion most recognized by MLP, with accuracy of 89.0%, while anger was the emotion most recognized by SVM, with accuracy of 87.4%. The most confusing emotions using MLP classification were happiness and fear, while for SVM, the most confusing emotions were disgust and fear. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/71237/1/MdSahSalam2016_SpeechemotionclassificationusingSVM.pdf Idris, Inshirah and Salam, Md. Sah and Sunar, Mohd. Shahrizal (2016) Speech emotion classification using SVM and MLP on prosodic and voice quality features. Jurnal Teknologi, 78 (2-2). pp. 27-33. ISSN 0127-9696 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960154852&doi=10.11113%2fjt.v78.6925&partnerID=40&md5=077bb5e73345f665103c0fb5d9df3473
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Idris, Inshirah
Salam, Md. Sah
Sunar, Mohd. Shahrizal
Speech emotion classification using SVM and MLP on prosodic and voice quality features
description In this paper, a comparison of emotion classification undertaken by the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP) Neural Network, using prosodic and voice quality features extracted from the Berlin Emotional Database, is reported. The features were extracted using PRAAT tools, while the WEKA tool was used for classification. Different parameters were set up for both SVM and MLP, which are used to obtain an optimized emotion classification. The results show that MLP overcomes SVM in overall emotion classification performance. Nevertheless, the training for SVM was much faster when compared to MLP. The overall accuracy was 76.82% for SVM and 78.69% for MLP. Sadness was the emotion most recognized by MLP, with accuracy of 89.0%, while anger was the emotion most recognized by SVM, with accuracy of 87.4%. The most confusing emotions using MLP classification were happiness and fear, while for SVM, the most confusing emotions were disgust and fear.
format Article
author Idris, Inshirah
Salam, Md. Sah
Sunar, Mohd. Shahrizal
author_facet Idris, Inshirah
Salam, Md. Sah
Sunar, Mohd. Shahrizal
author_sort Idris, Inshirah
title Speech emotion classification using SVM and MLP on prosodic and voice quality features
title_short Speech emotion classification using SVM and MLP on prosodic and voice quality features
title_full Speech emotion classification using SVM and MLP on prosodic and voice quality features
title_fullStr Speech emotion classification using SVM and MLP on prosodic and voice quality features
title_full_unstemmed Speech emotion classification using SVM and MLP on prosodic and voice quality features
title_sort speech emotion classification using svm and mlp on prosodic and voice quality features
publisher Penerbit UTM Press
publishDate 2016
url http://eprints.utm.my/id/eprint/71237/1/MdSahSalam2016_SpeechemotionclassificationusingSVM.pdf
http://eprints.utm.my/id/eprint/71237/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960154852&doi=10.11113%2fjt.v78.6925&partnerID=40&md5=077bb5e73345f665103c0fb5d9df3473
_version_ 1643656144609083392
score 13.211869

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