Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model

Speaker and speech variability are a challenge in speaker and speech recognition. In the context of Malaysian English speakers, the variability is highly complex due to sociolinguistic and cultural background. Past researches focused on vowel classification of Malaysian English on a small dataset wi...

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Main Author:	Goh, Eng Lyn
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/35850/5/GohEngLynMFSKSM2013.pdf
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id	my-utm-ep.35850
record_format	uketd_dc
spelling	my-utm-ep.358502017-07-13T06:19:11Z Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model 2013-06 Goh, Eng Lyn TK Electrical engineering. Electronics Nuclear engineering Speaker and speech variability are a challenge in speaker and speech recognition. In the context of Malaysian English speakers, the variability is highly complex due to sociolinguistic and cultural background. Past researches focused on vowel classification of Malaysian English on a small dataset with limited number of speakers and text corpus. Among speaker specific characteristics, gender is the most prominent features, which is followed by accents. This project approached the issues of speaker and speech variability in Malaysian English by proposing identifier that combined the gender and accent aspects. This method is fulfilled by training the classifier with gender-dependent data and accent-prone text corpus. The genderaccent database collected is comprised of 120 speakers categorized into four genderaccent groups namely Malay Female (CF), Malay Male (MM), Chinese Female (CF) and Chinese Male (CM). MFCC algorithm is used to extract the features, while GMM is the algorithm used to model the identifier. Findings from the test results show that female and Chinese speakers have higher degree of distinctiveness compared to other accent-gender groups. Chinese female is the best recognized accent-gender group, meanwhile Malay Male is the least recognized, due to codemixing of Malay language and English. Optimum configuration of GMM is also studied across 3 different numbers of Gaussians (12, 24, and 32). It is found that 24 is the most optimal configuration of MFCC-GMM. Meanwhile, it is also known that MFCC-GMM performs better than LPC-KNN on noisy dataset. Overall, the MFCCGMM identifier scored 99.34% gender identification rate, 67.5% accent identification and 65.83% for accent-gender identification task. 2013-06 Thesis http://eprints.utm.my/id/eprint/35850/ http://eprints.utm.my/id/eprint/35850/5/GohEngLynMFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70422?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Goh, Eng Lyn Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model
description	Speaker and speech variability are a challenge in speaker and speech recognition. In the context of Malaysian English speakers, the variability is highly complex due to sociolinguistic and cultural background. Past researches focused on vowel classification of Malaysian English on a small dataset with limited number of speakers and text corpus. Among speaker specific characteristics, gender is the most prominent features, which is followed by accents. This project approached the issues of speaker and speech variability in Malaysian English by proposing identifier that combined the gender and accent aspects. This method is fulfilled by training the classifier with gender-dependent data and accent-prone text corpus. The genderaccent database collected is comprised of 120 speakers categorized into four genderaccent groups namely Malay Female (CF), Malay Male (MM), Chinese Female (CF) and Chinese Male (CM). MFCC algorithm is used to extract the features, while GMM is the algorithm used to model the identifier. Findings from the test results show that female and Chinese speakers have higher degree of distinctiveness compared to other accent-gender groups. Chinese female is the best recognized accent-gender group, meanwhile Malay Male is the least recognized, due to codemixing of Malay language and English. Optimum configuration of GMM is also studied across 3 different numbers of Gaussians (12, 24, and 32). It is found that 24 is the most optimal configuration of MFCC-GMM. Meanwhile, it is also known that MFCC-GMM performs better than LPC-KNN on noisy dataset. Overall, the MFCCGMM identifier scored 99.34% gender identification rate, 67.5% accent identification and 65.83% for accent-gender identification task.
format	Thesis
qualification_level	Master's degree
author	Goh, Eng Lyn
author_facet	Goh, Eng Lyn
author_sort	Goh, Eng Lyn
title	Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model
title_short	Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model
title_full	Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model
title_fullStr	Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model
title_full_unstemmed	Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model
title_sort	gender and accent identification for malaysian english using mfcc and gaussian mixture model
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/35850/5/GohEngLynMFSKSM2013.pdf
_version_	1747816371009355776

Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model

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