Speaker identification using distributed vector quantization and Gaussian mixture models

Speaker identification is the computing task of recognizing people's identity based on their voices. There are two main difficulties in this field. First is how to maintain the accuracy rate under large amount of training data. Second is how to reduce the processing time. Previous studies repor...

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Main Author:	Loh, Mun Yee
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11585/6/LohMunYeeMFSKSM2010.pdf
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id	my-utm-ep.11585
record_format	uketd_dc
spelling	my-utm-ep.115852017-09-28T00:21:43Z Speaker identification using distributed vector quantization and Gaussian mixture models 2010-03 Loh, Mun Yee QA75 Electronic computers. Computer science Speaker identification is the computing task of recognizing people's identity based on their voices. There are two main difficulties in this field. First is how to maintain the accuracy rate under large amount of training data. Second is how to reduce the processing time. Previous studies reported that Gaussian Mixture Model (GMM) for speaker identification appears to have many advantages. However, due to long processing time, this process does not always produce satisfying result in practice. Meanwhile, current mechanisms for hybrid production of speaker identification are directed more towards accuracy problems, not processing time optimization. This research focuses on constructing distributed data training on Vector Quantization (VQ) modeling to achieve an initial result. The decision tree approach is applied to obtain distributed training for VQ model. GMM classification process is then employed on the initial result to achieve a final result. The efficiency of the model is evaluated by computational time and accuracy rate compared to GMM baseline models. Experimental result shows that the hybrid distributed VQ/GMM model yields better accuracy. Besides, it gives 80% reduction in processing time and is 5 times faster compared to GMM baseline models. In conclusion, this research successfully improves the computational time and accuracy of the text-independent speaker identification system. 2010-03 Thesis http://eprints.utm.my/id/eprint/11585/ http://eprints.utm.my/id/eprint/11585/6/LohMunYeeMFSKSM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Loh, Mun Yee Speaker identification using distributed vector quantization and Gaussian mixture models
description	Speaker identification is the computing task of recognizing people's identity based on their voices. There are two main difficulties in this field. First is how to maintain the accuracy rate under large amount of training data. Second is how to reduce the processing time. Previous studies reported that Gaussian Mixture Model (GMM) for speaker identification appears to have many advantages. However, due to long processing time, this process does not always produce satisfying result in practice. Meanwhile, current mechanisms for hybrid production of speaker identification are directed more towards accuracy problems, not processing time optimization. This research focuses on constructing distributed data training on Vector Quantization (VQ) modeling to achieve an initial result. The decision tree approach is applied to obtain distributed training for VQ model. GMM classification process is then employed on the initial result to achieve a final result. The efficiency of the model is evaluated by computational time and accuracy rate compared to GMM baseline models. Experimental result shows that the hybrid distributed VQ/GMM model yields better accuracy. Besides, it gives 80% reduction in processing time and is 5 times faster compared to GMM baseline models. In conclusion, this research successfully improves the computational time and accuracy of the text-independent speaker identification system.
format	Thesis
qualification_level	Master's degree
author	Loh, Mun Yee
author_facet	Loh, Mun Yee
author_sort	Loh, Mun Yee
title	Speaker identification using distributed vector quantization and Gaussian mixture models
title_short	Speaker identification using distributed vector quantization and Gaussian mixture models
title_full	Speaker identification using distributed vector quantization and Gaussian mixture models
title_fullStr	Speaker identification using distributed vector quantization and Gaussian mixture models
title_full_unstemmed	Speaker identification using distributed vector quantization and Gaussian mixture models
title_sort	speaker identification using distributed vector quantization and gaussian mixture models
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/11585/6/LohMunYeeMFSKSM2010.pdf
_version_	1747814874107346944

Speaker identification using distributed vector quantization and Gaussian mixture models

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