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:	http://ir.uitm.edu.my/id/eprint/1496/ http://ir.uitm.edu.my/id/eprint/1496/1/TD_IRDHAN%20SARBINI%20CS%2005_5.pdf

id	uitm-1496
recordtype	eprints
spelling	uitm-14962017-05-30T07:32:33Z http://ir.uitm.edu.my/id/eprint/1496/ Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini 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 NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/1496/1/TD_IRDHAN%20SARBINI%20CS%2005_5.pdf Sarbini, Irdhan (2005) Development of isolated Malay words speech recognition prototype using recurrent neural network / Irdhan Sarbini. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
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
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
publishDate	2005
url	http://ir.uitm.edu.my/id/eprint/1496/ http://ir.uitm.edu.my/id/eprint/1496/1/TD_IRDHAN%20SARBINI%20CS%2005_5.pdf
first_indexed	2023-09-18T22:45:56Z
last_indexed	2023-09-18T22:45:56Z
_version_	1777417224016887808

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

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