English digits speech recognition system based on Hidden Markov Models

This paper aims to design and implement English digits speech recognition system using Matlab (GUI). This work was based on the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech wavefo...

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Main Authors:	Abushariah, Ahmad A. M., Gunawan, Teddy Surya, Khalifa, Othman Omran, Abushariah, Mohammad Abd-Alrahman Mahmoud
Format:	Conference or Workshop Item
Language:	English
Published:	2010
Subjects:	TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Online Access:	http://irep.iium.edu.my/2328/ http://irep.iium.edu.my/2328/ http://irep.iium.edu.my/2328/4/English_Digits_Speech_Recognition_System_Based_on_Hidden_Markov_Models.pdf

id	iium-2328
recordtype	eprints
spelling	iium-23282011-11-24T06:00:48Z http://irep.iium.edu.my/2328/ English digits speech recognition system based on Hidden Markov Models Abushariah, Ahmad A. M. Gunawan, Teddy Surya Khalifa, Othman Omran Abushariah, Mohammad Abd-Alrahman Mahmoud TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices This paper aims to design and implement English digits speech recognition system using Matlab (GUI). This work was based on the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique This paper focuses on all English digits from (Zero through Nine), which is based on isolated words structure. Two modules were developed, namely the isolated words speech recognition and the continuous speech recognition. Both modules were tested in both clean and noisy environments and showed a successful recognition rates. In clean environment and isolated words speech recognition module, the multi-speaker mode achieved 99.5% whereas the speaker-independent mode achieved 79.5%. In clean environment and continuous speech recognition module, the multi-speaker mode achieved 72.5% whereas the speaker-independent mode achieved 56.25%. However in noisy environment and isolated words speech recognition module, the multi-speaker mode achieved 88% whereas the speaker-independent mode achieved 67%. In noisy environment and continuous speech recognition module, the multi-speaker mode achieved 82.5% whereas the speaker-independent mode achieved 76.67%. These recognition rates are relatively successful if compared to similar systems. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/2328/4/English_Digits_Speech_Recognition_System_Based_on_Hidden_Markov_Models.pdf Abushariah, Ahmad A. M. and Gunawan, Teddy Surya and Khalifa, Othman Omran and Abushariah, Mohammad Abd-Alrahman Mahmoud (2010) English digits speech recognition system based on Hidden Markov Models. In: International Conference on Computer and Communication Engineering ICCCE 2010, 11-13 May, 2010, Kuala Lumpur, Malaysia. http://www.iium.edu.my/iccce/10/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Abushariah, Ahmad A. M. Gunawan, Teddy Surya Khalifa, Othman Omran Abushariah, Mohammad Abd-Alrahman Mahmoud English digits speech recognition system based on Hidden Markov Models
description	This paper aims to design and implement English digits speech recognition system using Matlab (GUI). This work was based on the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique This paper focuses on all English digits from (Zero through Nine), which is based on isolated words structure. Two modules were developed, namely the isolated words speech recognition and the continuous speech recognition. Both modules were tested in both clean and noisy environments and showed a successful recognition rates. In clean environment and isolated words speech recognition module, the multi-speaker mode achieved 99.5% whereas the speaker-independent mode achieved 79.5%. In clean environment and continuous speech recognition module, the multi-speaker mode achieved 72.5% whereas the speaker-independent mode achieved 56.25%. However in noisy environment and isolated words speech recognition module, the multi-speaker mode achieved 88% whereas the speaker-independent mode achieved 67%. In noisy environment and continuous speech recognition module, the multi-speaker mode achieved 82.5% whereas the speaker-independent mode achieved 76.67%. These recognition rates are relatively successful if compared to similar systems.
format	Conference or Workshop Item
author	Abushariah, Ahmad A. M. Gunawan, Teddy Surya Khalifa, Othman Omran Abushariah, Mohammad Abd-Alrahman Mahmoud
author_facet	Abushariah, Ahmad A. M. Gunawan, Teddy Surya Khalifa, Othman Omran Abushariah, Mohammad Abd-Alrahman Mahmoud
author_sort	Abushariah, Ahmad A. M.
title	English digits speech recognition system based on Hidden Markov Models
title_short	English digits speech recognition system based on Hidden Markov Models
title_full	English digits speech recognition system based on Hidden Markov Models
title_fullStr	English digits speech recognition system based on Hidden Markov Models
title_full_unstemmed	English digits speech recognition system based on Hidden Markov Models
title_sort	english digits speech recognition system based on hidden markov models
publishDate	2010
url	http://irep.iium.edu.my/2328/ http://irep.iium.edu.my/2328/ http://irep.iium.edu.my/2328/4/English_Digits_Speech_Recognition_System_Based_on_Hidden_Markov_Models.pdf
first_indexed	2023-09-18T20:09:54Z
last_indexed	2023-09-18T20:09:54Z
_version_	1777407407877521408

English digits speech recognition system based on Hidden Markov Models

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