Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini

In this research, a prototype of isolated Malay word speech recognition using recurrent neural network (RNN) is proposed. The research is working on speaker independent, which is combination of male and female respondent. A simple three-layer RNN which is Elman Network is employed to learn the pa...

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Main Author:	Sarbini, Irdhan
Format:	Thesis
Language:	English
Published:	2005
Online Access:	https://ir.uitm.edu.my/id/eprint/1496/1/TD_IRDHAN%20SARBINI%20CS%2005_5.pdf
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id	my-uitm-ir.1496
record_format	uketd_dc
spelling	my-uitm-ir.14962017-05-30T07:32:33Z Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini 2005 Sarbini, Irdhan In this research, a prototype of isolated Malay word speech recognition using recurrent neural network (RNN) is proposed. The research is working on speaker independent, which is combination of male and female respondent. A simple three-layer RNN which is Elman Network is employed to learn the pattern of speech features. Melfrequency Cepstral Coefficient (MFCC) feature is selected and the features are extracted by using Speech Filing System freeware application. Experiments are performed to determine the optimal number of hidden neurons for the architecture of RNN. The total recognition rate is 95 %. This research also reveals that RNN is able to give good performance for speech recognition and for incomplete data. 2005 Thesis https://ir.uitm.edu.my/id/eprint/1496/ https://ir.uitm.edu.my/id/eprint/1496/1/TD_IRDHAN%20SARBINI%20CS%2005_5.pdf text en public other degree Universiti Teknologi MARA Faculty of Information Technology and Quantitative Sciences
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
description	In this research, a prototype of isolated Malay word speech recognition using recurrent neural network (RNN) is proposed. The research is working on speaker independent, which is combination of male and female respondent. A simple three-layer RNN which is Elman Network is employed to learn the pattern of speech features. Melfrequency Cepstral Coefficient (MFCC) feature is selected and the features are extracted by using Speech Filing System freeware application. Experiments are performed to determine the optimal number of hidden neurons for the architecture of RNN. The total recognition rate is 95 %. This research also reveals that RNN is able to give good performance for speech recognition and for incomplete data.
format	Thesis
qualification_name	other
qualification_level	Bachelor degree
author	Sarbini, Irdhan
spellingShingle	Sarbini, Irdhan Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini
author_facet	Sarbini, Irdhan
author_sort	Sarbini, Irdhan
title	Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini
title_short	Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini
title_full	Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini
title_fullStr	Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini
title_full_unstemmed	Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini
title_sort	development of isolated malay words speech recognition prototype using recurrent neural network / irdhan sarbini
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Information Technology and Quantitative Sciences
publishDate	2005
url	https://ir.uitm.edu.my/id/eprint/1496/1/TD_IRDHAN%20SARBINI%20CS%2005_5.pdf
_version_	1783733022397300736

Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini

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