Computer-based stuttered speech detection system using Hidden Markov Model

Stuttering has attracted extensive research interests over the past decades. Most of the available stuttering diagnostics and assessment technique uses human perceptual judgment to overt stuttered speech characteristics. Conventionally, the stuttering severity is diagnosed by manual counting the num...

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Main Author:	Chin, Wee Lip
Format:	Thesis
Language:	English
Published:	2012
Subjects:	QH301 Biology
Online Access:	http://eprints.utm.my/id/eprint/78550/1/ChinWeeLipMFBME2012.pdf
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id	my-utm-ep.78550
record_format	uketd_dc
spelling	my-utm-ep.785502018-08-27T03:22:25Z Computer-based stuttered speech detection system using Hidden Markov Model 2012-08 Chin, Wee Lip QH301 Biology Stuttering has attracted extensive research interests over the past decades. Most of the available stuttering diagnostics and assessment technique uses human perceptual judgment to overt stuttered speech characteristics. Conventionally, the stuttering severity is diagnosed by manual counting the number of occurrences of disfluencies of pre-recorded therapist-patient conversation. It is a time-consuming task, subjective, inconsistent and easily prone to error across clinics. Therefore, this thesis proposes a computerized system by deploying HMM-based speech recognition technique to detect the stuttered speech disfluency. The continuous Malay digit string has been used as the training and testing set for fluency detection. Hidden Markov Model (HMM) is a robust and powerful statistical-based acoustic modeling technique. With their efficient training algorithm (Forward-backward, Baum-Welch algorithms) and recognition algorithm, as well as its modeling flexibility in model topology and other knowledge sources, HMM has been successfully applied in solving various tasks. In this thesis, a set of normal voice for digit string as database is used for training HMM. Then, the pseudo stuttering voice was collected as testing set for proposed system. The generated experimental results were compared with the results made by Speech Language Pathologist (SLP) from Clinic of Audiology and Speech Sciences of Universiti Kebangsaan Malaysia (UKM). As a result, the proposed system is proven to be capable to achieve 100% average syllable repetition detection accuracy with 86.605% average sound prolongation detection accuracy. The SLP agreed with the result generated by the software. This system can be further enhanced for detecting stuttering disorder for daily speaking words where Microsoft Visual C++ 6.0 and Goldwave have been used for developing the software which can be executed under the window-based environment. 2012-08 Thesis http://eprints.utm.my/id/eprint/78550/ http://eprints.utm.my/id/eprint/78550/1/ChinWeeLipMFBME2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:110409 masters Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering Faculty of Biosciences and Medical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QH301 Biology
spellingShingle	QH301 Biology Chin, Wee Lip Computer-based stuttered speech detection system using Hidden Markov Model
description	Stuttering has attracted extensive research interests over the past decades. Most of the available stuttering diagnostics and assessment technique uses human perceptual judgment to overt stuttered speech characteristics. Conventionally, the stuttering severity is diagnosed by manual counting the number of occurrences of disfluencies of pre-recorded therapist-patient conversation. It is a time-consuming task, subjective, inconsistent and easily prone to error across clinics. Therefore, this thesis proposes a computerized system by deploying HMM-based speech recognition technique to detect the stuttered speech disfluency. The continuous Malay digit string has been used as the training and testing set for fluency detection. Hidden Markov Model (HMM) is a robust and powerful statistical-based acoustic modeling technique. With their efficient training algorithm (Forward-backward, Baum-Welch algorithms) and recognition algorithm, as well as its modeling flexibility in model topology and other knowledge sources, HMM has been successfully applied in solving various tasks. In this thesis, a set of normal voice for digit string as database is used for training HMM. Then, the pseudo stuttering voice was collected as testing set for proposed system. The generated experimental results were compared with the results made by Speech Language Pathologist (SLP) from Clinic of Audiology and Speech Sciences of Universiti Kebangsaan Malaysia (UKM). As a result, the proposed system is proven to be capable to achieve 100% average syllable repetition detection accuracy with 86.605% average sound prolongation detection accuracy. The SLP agreed with the result generated by the software. This system can be further enhanced for detecting stuttering disorder for daily speaking words where Microsoft Visual C++ 6.0 and Goldwave have been used for developing the software which can be executed under the window-based environment.
format	Thesis
qualification_level	Master's degree
author	Chin, Wee Lip
author_facet	Chin, Wee Lip
author_sort	Chin, Wee Lip
title	Computer-based stuttered speech detection system using Hidden Markov Model
title_short	Computer-based stuttered speech detection system using Hidden Markov Model
title_full	Computer-based stuttered speech detection system using Hidden Markov Model
title_fullStr	Computer-based stuttered speech detection system using Hidden Markov Model
title_full_unstemmed	Computer-based stuttered speech detection system using Hidden Markov Model
title_sort	computer-based stuttered speech detection system using hidden markov model
granting_institution	Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering
granting_department	Faculty of Biosciences and Medical Engineering
publishDate	2012
url	http://eprints.utm.my/id/eprint/78550/1/ChinWeeLipMFBME2012.pdf
_version_	1747818012766896128

Computer-based stuttered speech detection system using Hidden Markov Model

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