Speech emotion recognition using kNN classifier
The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Po...
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2013
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my.unimap-291762013-10-25T14:39:35Z Speech emotion recognition using kNN classifier M. N., Hasrul Hariharan, Muthusamy Sazali, Yaacob, Prof. Dr. kids.hasrul@gmail.com Speech emotion recognition Mel-Frequency Cepstrum Coefficient (MFCC) Linear Predictive Coefficients (LPC) Linear Prediction Cepstral Coefficients (LPCC) Weighted Linear Prediction Cepstral Coefficients (WLPCC) linear discriminant analysis (LDA) Principle component analysis (PCA) k-nearest neighbour (kNN) The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia. There are many methods that are commonly used by researches in order to recognize speech of human emotion. In this paper, Surrey audio-visual expressed emotion (SAVEE) database are used for the analysis. The Mel-Frequency Cepstrum Coefficient (MFCC), Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) are extracted from these speech signals. Principle component analysis (PCA) and linear discriminant analysis (LDA) is method used to selected features. Seven typical emotions for SAVEE classified with k-nearest neighbour (kNN). 2013-10-25T14:39:35Z 2013-10-25T14:39:35Z 2012-06-18 Working Paper p. 1073-1076 978-967-5760-11-2 http://hdl.handle.net/123456789/29176 en Proceedings of the the 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012); Universiti Malaysia Perlis (UniMAP) |
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Speech emotion recognition Mel-Frequency Cepstrum Coefficient (MFCC) Linear Predictive Coefficients (LPC) Linear Prediction Cepstral Coefficients (LPCC) Weighted Linear Prediction Cepstral Coefficients (WLPCC) linear discriminant analysis (LDA) Principle component analysis (PCA) k-nearest neighbour (kNN) |
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Speech emotion recognition Mel-Frequency Cepstrum Coefficient (MFCC) Linear Predictive Coefficients (LPC) Linear Prediction Cepstral Coefficients (LPCC) Weighted Linear Prediction Cepstral Coefficients (WLPCC) linear discriminant analysis (LDA) Principle component analysis (PCA) k-nearest neighbour (kNN) M. N., Hasrul Hariharan, Muthusamy Sazali, Yaacob, Prof. Dr. Speech emotion recognition using kNN classifier |
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The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia. |
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kids.hasrul@gmail.com |
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kids.hasrul@gmail.com M. N., Hasrul Hariharan, Muthusamy Sazali, Yaacob, Prof. Dr. |
format |
Working Paper |
author |
M. N., Hasrul Hariharan, Muthusamy Sazali, Yaacob, Prof. Dr. |
author_sort |
M. N., Hasrul |
title |
Speech emotion recognition using kNN classifier |
title_short |
Speech emotion recognition using kNN classifier |
title_full |
Speech emotion recognition using kNN classifier |
title_fullStr |
Speech emotion recognition using kNN classifier |
title_full_unstemmed |
Speech emotion recognition using kNN classifier |
title_sort |
speech emotion recognition using knn classifier |
publisher |
Universiti Malaysia Perlis (UniMAP) |
publishDate |
2013 |
url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/29176 |
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1643795648368082944 |
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13.211869 |