Speaker identification using hybrid of subtractive clustering and radial basis function

Speaker identification is the computing task of identifying unknown identities based on voice. A good speaker identification system must have a high accuracy rate to prevent incorrect detection of the user's identity. This research proposed a hybrid of Subtractive Clustering and Radial Basis Fu...

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Main Author:	Yap, Teck Ann
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/37059/5/YapTeckAnnMFSKSM2013.pdf
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id	my-utm-ep.37059
record_format	uketd_dc
spelling	my-utm-ep.370592017-07-18T04:05:06Z Speaker identification using hybrid of subtractive clustering and radial basis function 2013-06 Yap, Teck Ann TK Electrical engineering. Electronics Nuclear engineering Speaker identification is the computing task of identifying unknown identities based on voice. A good speaker identification system must have a high accuracy rate to prevent incorrect detection of the user's identity. This research proposed a hybrid of Subtractive Clustering and Radial Basis Function (Sub-RBF) which is the combination of supervised and unsupervised learning. Unsupervised learning is more suitable for learning large and complex models than supervised learning. This is because supervised learning increasing the number of connections between sets in the network. If the model contains a large and complex dataset, supervised learning is difficult. In addition, K-means is faced with improper initial guessing of first cluster centre and difficulty in determining the number of cluster centres. The proposed technique is introduced because subtractive clustering is able to solve these problems. RBF is a simple network structures with fast learning algorithm. RBF neural network model with subtractive clustering proposed to select hidden node centers, can achieve faster training speed. In the meantime, the RBF network was trained with a regularization parameter so as to minimize the variances of the nodes in the hidden layer and perform more accurate prediction. The accuracy rate for subtractive clustering is 8.125% and 11.25% for training dataset 1 and training dataset 2 respectively. However, Sub-RBF provides 76.875% and 71.25% accuracy rate for training dataset 1 and training dataset 2 respectively. In conclusion, Sub-RBF has improved the speaker identification system accuracy rate. 2013-06 Thesis http://eprints.utm.my/id/eprint/37059/ http://eprints.utm.my/id/eprint/37059/5/YapTeckAnnMFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69999?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Yap, Teck Ann Speaker identification using hybrid of subtractive clustering and radial basis function
description	Speaker identification is the computing task of identifying unknown identities based on voice. A good speaker identification system must have a high accuracy rate to prevent incorrect detection of the user's identity. This research proposed a hybrid of Subtractive Clustering and Radial Basis Function (Sub-RBF) which is the combination of supervised and unsupervised learning. Unsupervised learning is more suitable for learning large and complex models than supervised learning. This is because supervised learning increasing the number of connections between sets in the network. If the model contains a large and complex dataset, supervised learning is difficult. In addition, K-means is faced with improper initial guessing of first cluster centre and difficulty in determining the number of cluster centres. The proposed technique is introduced because subtractive clustering is able to solve these problems. RBF is a simple network structures with fast learning algorithm. RBF neural network model with subtractive clustering proposed to select hidden node centers, can achieve faster training speed. In the meantime, the RBF network was trained with a regularization parameter so as to minimize the variances of the nodes in the hidden layer and perform more accurate prediction. The accuracy rate for subtractive clustering is 8.125% and 11.25% for training dataset 1 and training dataset 2 respectively. However, Sub-RBF provides 76.875% and 71.25% accuracy rate for training dataset 1 and training dataset 2 respectively. In conclusion, Sub-RBF has improved the speaker identification system accuracy rate.
format	Thesis
qualification_level	Master's degree
author	Yap, Teck Ann
author_facet	Yap, Teck Ann
author_sort	Yap, Teck Ann
title	Speaker identification using hybrid of subtractive clustering and radial basis function
title_short	Speaker identification using hybrid of subtractive clustering and radial basis function
title_full	Speaker identification using hybrid of subtractive clustering and radial basis function
title_fullStr	Speaker identification using hybrid of subtractive clustering and radial basis function
title_full_unstemmed	Speaker identification using hybrid of subtractive clustering and radial basis function
title_sort	speaker identification using hybrid of subtractive clustering and radial basis function
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/37059/5/YapTeckAnnMFSKSM2013.pdf
_version_	1747816497412046848

Speaker identification using hybrid of subtractive clustering and radial basis function

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