Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network

Stuttering is characterized by disfluencies, which disrupt the flow of speech. Traditional way of stuttering assessment is time consuming. The stuttering assessment results always inconsistent between different judges, because human perception on the stuttering event are different for each individua...

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Main Author:	Choo , Chian Choong
Format:	Thesis
Language:	English
Published:	2016
Subjects:	TK7800-8360 Electronics
Online Access:	http://eprints.usm.my/41198/1/CHOO_CHIAN_CHOONG_24_Pages.pdf
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id	my-usm-ep.41198
record_format	uketd_dc
spelling	my-usm-ep.411982018-07-31T02:59:29Z Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network 2016 Choo , Chian Choong TK7800-8360 Electronics Stuttering is characterized by disfluencies, which disrupt the flow of speech. Traditional way of stuttering assessment is time consuming. The stuttering assessment results always inconsistent between different judges, because human perception on the stuttering event are different for each individual. The stuttering assessment system will reduce the tedious manual work and improve the consistency of the assessment result. The objective of this project is to develop classifier for prolongation and repetition disfluencies in speech using artificial neural network. Three different feature extraction was used in this project, which is Mel Frequency Cepstral Coefficient (MFCC), Linear Prediction Coefficient (LPC) and hybrid MFCC and LPC. The flow of the project were: 1) Stuttered speech data acquisition; 2) Word segmentation and categorization; 3) Feature extraction using 3 different methods; 4) Classification using neural pattern recognition in Matlab. The overall accuracy of the 3 different feature extraction used were 84.6% (LPC), 84.6% (MFCC) and 88.5% (hybrid MFCC and LPC). The classification accuracy using hybrid MFCC and LPC with respect to target classes, which were prolongation, repetition and fluent, were 66.7%, 92.3% and 96.3%. A disfluencies classifier had been developed with hybrid MFCC and LPC as feature extraction and ANN as a classifier. The overall performance of the disfluencies classifier is 88.5%. 2016 Thesis http://eprints.usm.my/41198/ http://eprints.usm.my/41198/1/CHOO_CHIAN_CHOONG_24_Pages.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK7800-8360 Electronics
spellingShingle	TK7800-8360 Electronics Choo , Chian Choong Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network
description	Stuttering is characterized by disfluencies, which disrupt the flow of speech. Traditional way of stuttering assessment is time consuming. The stuttering assessment results always inconsistent between different judges, because human perception on the stuttering event are different for each individual. The stuttering assessment system will reduce the tedious manual work and improve the consistency of the assessment result. The objective of this project is to develop classifier for prolongation and repetition disfluencies in speech using artificial neural network. Three different feature extraction was used in this project, which is Mel Frequency Cepstral Coefficient (MFCC), Linear Prediction Coefficient (LPC) and hybrid MFCC and LPC. The flow of the project were: 1) Stuttered speech data acquisition; 2) Word segmentation and categorization; 3) Feature extraction using 3 different methods; 4) Classification using neural pattern recognition in Matlab. The overall accuracy of the 3 different feature extraction used were 84.6% (LPC), 84.6% (MFCC) and 88.5% (hybrid MFCC and LPC). The classification accuracy using hybrid MFCC and LPC with respect to target classes, which were prolongation, repetition and fluent, were 66.7%, 92.3% and 96.3%. A disfluencies classifier had been developed with hybrid MFCC and LPC as feature extraction and ANN as a classifier. The overall performance of the disfluencies classifier is 88.5%.
format	Thesis
qualification_level	Master's degree
author	Choo , Chian Choong
author_facet	Choo , Chian Choong
author_sort	Choo , Chian Choong
title	Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network
title_short	Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network
title_full	Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network
title_fullStr	Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network
title_full_unstemmed	Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network
title_sort	hybrid mfcc and lpc for stuttering assessment using neural network
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
publishDate	2016
url	http://eprints.usm.my/41198/1/CHOO_CHIAN_CHOONG_24_Pages.pdf
_version_	1747820887548100608

Hybrid Mfcc And Lpc For Stuttering Assessment Using Neural Network

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