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Implementation of artificial neural network to recognize numbers from voice

Speech recognition is a subjective phenomenon which also an important part of human–machine interaction which still faces a lot of problem. The purpose of this work is to investigate and apply the artificial neural network (ANN) to recognise numbers using voice. In this work, MATLAB neural network t...

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Main Authors:	Fatin Nur Amalina, Zainol, Mohd Zamri, Ibrahim
Format:	Conference or Workshop Item
Language:	English English
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/42327/1/Implementation%20of%20artificial%20neural%20network.pdf http://umpir.ump.edu.my/id/eprint/42327/2/Implementation%20of%20artificial%20neural%20network%20to%20recognize%20numbers%20from%20voice_ABS.pdf http://umpir.ump.edu.my/id/eprint/42327/ https://doi.org/10.1007/978-981-16-8690-0_78
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id	my.ump.umpir.42327
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spelling	my.ump.umpir.423272024-10-30T04:30:32Z http://umpir.ump.edu.my/id/eprint/42327/ Implementation of artificial neural network to recognize numbers from voice Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Speech recognition is a subjective phenomenon which also an important part of human–machine interaction which still faces a lot of problem. The purpose of this work is to investigate and apply the artificial neural network (ANN) to recognise numbers using voice. In this work, MATLAB neural network toolbox is used to create, train and simulate the ANN. The dataset consisted a voice from ‘one’ to ‘five’ undergo windowing process to view a short time segment of a longer signal and analyse its frequency content and then being filtered by using a band-pass filter to remove the unwanted noise and been converted into histograms as an input for the network. From the experiments, the highest accuracy level obtained is 72.5% by using histograms as Feature Extraction. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42327/1/Implementation%20of%20artificial%20neural%20network.pdf pdf en http://umpir.ump.edu.my/id/eprint/42327/2/Implementation%20of%20artificial%20neural%20network%20to%20recognize%20numbers%20from%20voice_ABS.pdf Fatin Nur Amalina, Zainol and Mohd Zamri, Ibrahim (2022) Implementation of artificial neural network to recognize numbers from voice. In: Lecture Notes in Electrical Engineering. 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021 , 23 August 2021 , Kuantan. pp. 895-904., 842. ISSN 1876-1100 ISBN 978-981168689-4 (Published) https://doi.org/10.1007/978-981-16-8690-0_78
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim Implementation of artificial neural network to recognize numbers from voice
description	Speech recognition is a subjective phenomenon which also an important part of human–machine interaction which still faces a lot of problem. The purpose of this work is to investigate and apply the artificial neural network (ANN) to recognise numbers using voice. In this work, MATLAB neural network toolbox is used to create, train and simulate the ANN. The dataset consisted a voice from ‘one’ to ‘five’ undergo windowing process to view a short time segment of a longer signal and analyse its frequency content and then being filtered by using a band-pass filter to remove the unwanted noise and been converted into histograms as an input for the network. From the experiments, the highest accuracy level obtained is 72.5% by using histograms as Feature Extraction.
format	Conference or Workshop Item
author	Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim
author_facet	Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim
author_sort	Fatin Nur Amalina, Zainol
title	Implementation of artificial neural network to recognize numbers from voice
title_short	Implementation of artificial neural network to recognize numbers from voice
title_full	Implementation of artificial neural network to recognize numbers from voice
title_fullStr	Implementation of artificial neural network to recognize numbers from voice
title_full_unstemmed	Implementation of artificial neural network to recognize numbers from voice
title_sort	implementation of artificial neural network to recognize numbers from voice
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/42327/1/Implementation%20of%20artificial%20neural%20network.pdf http://umpir.ump.edu.my/id/eprint/42327/2/Implementation%20of%20artificial%20neural%20network%20to%20recognize%20numbers%20from%20voice_ABS.pdf http://umpir.ump.edu.my/id/eprint/42327/ https://doi.org/10.1007/978-981-16-8690-0_78
_version_	1822924722415861760
score	13.23243

Implementation of artificial neural network to recognize numbers from voice

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