Statistical modeling for speech recognition

The demand of intelligent machines that may recognize the spoken speech and respond in a natural voice has been driving speech research. The challenging in speech recognition systems due to the language nature where there are no clear boundaries between words, the phonetic beginning and ending are...

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Main Authors:	Khalifa, Othman Omran, El-Darymli, Khalid Khalil, Hassan Abdalla Hashim, Aisha, Daoud, Jamal Ibrahim
Format:	Article
Language:	English
Published:	IDOSI Publication 2013
Subjects:	T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/29656/ http://irep.iium.edu.my/29656/ http://irep.iium.edu.my/29656/ http://irep.iium.edu.my/29656/1/21.pdf

id	iium-29656
recordtype	eprints
spelling	iium-296562017-06-13T04:58:33Z http://irep.iium.edu.my/29656/ Statistical modeling for speech recognition Khalifa, Othman Omran El-Darymli, Khalid Khalil Hassan Abdalla Hashim, Aisha Daoud, Jamal Ibrahim T10.5 Communication of technical information The demand of intelligent machines that may recognize the spoken speech and respond in a natural voice has been driving speech research. The challenging in speech recognition systems due to the language nature where there are no clear boundaries between words, the phonetic beginning and ending are influenced by neighbouring words, in addition to the variability in different speakers speech: male or female, young or senior, loud or low speech, read or spontaneous, emotional or formal, fast or slow speaking rate and the speech signal can be affected with environment noise. To avoid these difficulties the data driven statistical approach based on large quantities of spoken data is used. The performance of speech recognition systems is still far worse than that of humans. This is partly caused by the use of poor statistical models. In this paper, a comprehensive study of statistical methods for speech and language processing are presented. The role of signal processing in creating a reliable feature set for the recognizer and the role of statistical methods in enabling the recognizer to recognize the words of the spoken input sentence as well as the meaning associated with the recognized word sequence were presented. IDOSI Publication 2013-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/29656/1/21.pdf Khalifa, Othman Omran and El-Darymli, Khalid Khalil and Hassan Abdalla Hashim, Aisha and Daoud, Jamal Ibrahim (2013) Statistical modeling for speech recognition. World Applied Sciences Journal (21). pp. 115-122. ISSN 1818-4952 http://www.idosi.org/wasj/WASJ21(mae)13/21.pdf 10.5829/idosi.wasj.2013.21.mae.99935
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T10.5 Communication of technical information
spellingShingle	T10.5 Communication of technical information Khalifa, Othman Omran El-Darymli, Khalid Khalil Hassan Abdalla Hashim, Aisha Daoud, Jamal Ibrahim Statistical modeling for speech recognition
description	The demand of intelligent machines that may recognize the spoken speech and respond in a natural voice has been driving speech research. The challenging in speech recognition systems due to the language nature where there are no clear boundaries between words, the phonetic beginning and ending are influenced by neighbouring words, in addition to the variability in different speakers speech: male or female, young or senior, loud or low speech, read or spontaneous, emotional or formal, fast or slow speaking rate and the speech signal can be affected with environment noise. To avoid these difficulties the data driven statistical approach based on large quantities of spoken data is used. The performance of speech recognition systems is still far worse than that of humans. This is partly caused by the use of poor statistical models. In this paper, a comprehensive study of statistical methods for speech and language processing are presented. The role of signal processing in creating a reliable feature set for the recognizer and the role of statistical methods in enabling the recognizer to recognize the words of the spoken input sentence as well as the meaning associated with the recognized word sequence were presented.
format	Article
author	Khalifa, Othman Omran El-Darymli, Khalid Khalil Hassan Abdalla Hashim, Aisha Daoud, Jamal Ibrahim
author_facet	Khalifa, Othman Omran El-Darymli, Khalid Khalil Hassan Abdalla Hashim, Aisha Daoud, Jamal Ibrahim
author_sort	Khalifa, Othman Omran
title	Statistical modeling for speech recognition
title_short	Statistical modeling for speech recognition
title_full	Statistical modeling for speech recognition
title_fullStr	Statistical modeling for speech recognition
title_full_unstemmed	Statistical modeling for speech recognition
title_sort	statistical modeling for speech recognition
publisher	IDOSI Publication
publishDate	2013
url	http://irep.iium.edu.my/29656/ http://irep.iium.edu.my/29656/ http://irep.iium.edu.my/29656/ http://irep.iium.edu.my/29656/1/21.pdf
first_indexed	2023-09-18T20:43:34Z
last_indexed	2023-09-18T20:43:34Z
_version_	1777409525809152000

Statistical modeling for speech recognition

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