Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus

This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences record...

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Main Authors:	Abushariah, Mohammad Abd-Alrahman Mahmoud, -, Raja Ainon, Zainuddin, Roziati, Elshafei, Moustafa, Khalifa, Othman Omran
Format:	Article
Language:	English English
Published:	Zarqa Private University, Jordan 2012
Subjects:	T Technology (General) T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/6980/ http://irep.iium.edu.my/6980/ http://irep.iium.edu.my/6980/1/Arabic_Speaker-Independent_Continuous__Arab_Journal.pdf http://irep.iium.edu.my/6980/4/Arabic_Speaker-Independent_Continuous_.pdf

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spelling	iium-69802012-04-24T00:14:59Z http://irep.iium.edu.my/6980/ Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus Abushariah, Mohammad Abd-Alrahman Mahmoud -, Raja Ainon Zainuddin, Roziati Elshafei, Moustafa Khalifa, Othman Omran T Technology (General) T10.5 Communication of technical information This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world. The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models (HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers but different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of 11.27% and 10.07% with and without diacritical marks respectively. For different speakers with similar sentences, the system obtained a word recognition accuracy of 95.92% and 96.29% and a WER of 5.78% and 5.45% with and without diacritical marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of 89.08% and 90.23% and a WER of 15.59% and 14.44% with and without diacritical marks respectively. Zarqa Private University, Jordan 2012-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/6980/1/Arabic_Speaker-Independent_Continuous__Arab_Journal.pdf application/pdf en http://irep.iium.edu.my/6980/4/Arabic_Speaker-Independent_Continuous_.pdf Abushariah, Mohammad Abd-Alrahman Mahmoud and -, Raja Ainon and Zainuddin, Roziati and Elshafei, Moustafa and Khalifa, Othman Omran (2012) Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus. International Arab Journal of Information Technology (IAJIT), 9 (1). pp. 84-93. ISSN 1683-3198 http://www.iajit.org/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General) T10.5 Communication of technical information
spellingShingle	T Technology (General) T10.5 Communication of technical information Abushariah, Mohammad Abd-Alrahman Mahmoud -, Raja Ainon Zainuddin, Roziati Elshafei, Moustafa Khalifa, Othman Omran Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus
description	This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world. The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models (HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers but different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of 11.27% and 10.07% with and without diacritical marks respectively. For different speakers with similar sentences, the system obtained a word recognition accuracy of 95.92% and 96.29% and a WER of 5.78% and 5.45% with and without diacritical marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of 89.08% and 90.23% and a WER of 15.59% and 14.44% with and without diacritical marks respectively.
format	Article
author	Abushariah, Mohammad Abd-Alrahman Mahmoud -, Raja Ainon Zainuddin, Roziati Elshafei, Moustafa Khalifa, Othman Omran
author_facet	Abushariah, Mohammad Abd-Alrahman Mahmoud -, Raja Ainon Zainuddin, Roziati Elshafei, Moustafa Khalifa, Othman Omran
author_sort	Abushariah, Mohammad Abd-Alrahman Mahmoud
title	Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus
title_short	Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus
title_full	Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus
title_fullStr	Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus
title_full_unstemmed	Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus
title_sort	arabic speaker-independent continuous automatic speech recognition based on a phonetically rich and balanced speech corpus
publisher	Zarqa Private University, Jordan
publishDate	2012
url	http://irep.iium.edu.my/6980/ http://irep.iium.edu.my/6980/ http://irep.iium.edu.my/6980/1/Arabic_Speaker-Independent_Continuous__Arab_Journal.pdf http://irep.iium.edu.my/6980/4/Arabic_Speaker-Independent_Continuous_.pdf
first_indexed	2023-09-18T20:16:11Z
last_indexed	2023-09-18T20:16:11Z
_version_	1777407802897072128

Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus

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