Accent identification of Malaysian and Nigerian English based on acoustic features

Purpose - This paper studies acoustics features of energy, pitch and formants of Malaysian and Nigerian English vowels with the aim of effective accents identification using multi liner regression (MLR) and linear discriminant analysis (LDA) classifiers for performance improvement of ASR when expos...

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Main Authors: Atanda, Abdulwahab F., Mohd Yusof, Shahrul Azmi, Husni, Husniza
格式: Conference or Workshop Item
語言:English
出版: 2017
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spelling my.uum.repo.244942018-07-30T01:09:51Z http://repo.uum.edu.my/24494/ Accent identification of Malaysian and Nigerian English based on acoustic features Atanda, Abdulwahab F. Mohd Yusof, Shahrul Azmi Husni, Husniza QA75 Electronic computers. Computer science Purpose - This paper studies acoustics features of energy, pitch and formants of Malaysian and Nigerian English vowels with the aim of effective accents identification using multi liner regression (MLR) and linear discriminant analysis (LDA) classifiers for performance improvement of ASR when exposed to accented speech.Accent being a foremost source of ASR performance degradation has received a great attention from ASR researchers.Majority of ASR applications were developed with native English speakers speech samples without considering fact that most of its potential users speaks English as a second language with a marked accent, hence its poor performance when exposed to accented speech. Previous studies on accent has shown that the ability to correctly recognized accent has greatly enhanced the recognition performance of ASR when exposed to accented speech data.In a study of 14 regional accents of British, (Hanani, Russell, & Carey, 2013) achieved a performance increase of 5.58%.A study by (Vergyri, Lamel, & Gauvain, 2010) using six different regional accented English shows an average of 41.43% WER.Which was reduced to 27% on the incorporation of accent identification module.Several studies have explored several acoustic features of speech such as energy, pitch, formants, MFCC, and LPC to establish the differences between regional or cross ethnics accent aimed at better understanding of the differences in the acoustic features to enhance ASR performance.Apparently from the previous studies reviewed above, it is evident that accent constitute a hurdle to the performance of ASR. Hence, consequently serves as a barrier to ASR wide reception and usage in real life situations. Consequently, it becomes pertinent that accent should be given adequate research attention with the view of enhancing ASR performance to accented speech which will inherently promotes its wide acceptability and applicability globally. 2017-12-04 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/24494/1/SICONSEM%202017%2028%2030.pdf Atanda, Abdulwahab F. and Mohd Yusof, Shahrul Azmi and Husni, Husniza (2017) Accent identification of Malaysian and Nigerian English based on acoustic features. In: Sintok International Conference on Social Science and Management (SICONSEM 2017), 4-5 December 2017, Adya Hotel, Langkawi Island, Kedah, Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Atanda, Abdulwahab F.
Mohd Yusof, Shahrul Azmi
Husni, Husniza
Accent identification of Malaysian and Nigerian English based on acoustic features
description Purpose - This paper studies acoustics features of energy, pitch and formants of Malaysian and Nigerian English vowels with the aim of effective accents identification using multi liner regression (MLR) and linear discriminant analysis (LDA) classifiers for performance improvement of ASR when exposed to accented speech.Accent being a foremost source of ASR performance degradation has received a great attention from ASR researchers.Majority of ASR applications were developed with native English speakers speech samples without considering fact that most of its potential users speaks English as a second language with a marked accent, hence its poor performance when exposed to accented speech. Previous studies on accent has shown that the ability to correctly recognized accent has greatly enhanced the recognition performance of ASR when exposed to accented speech data.In a study of 14 regional accents of British, (Hanani, Russell, & Carey, 2013) achieved a performance increase of 5.58%.A study by (Vergyri, Lamel, & Gauvain, 2010) using six different regional accented English shows an average of 41.43% WER.Which was reduced to 27% on the incorporation of accent identification module.Several studies have explored several acoustic features of speech such as energy, pitch, formants, MFCC, and LPC to establish the differences between regional or cross ethnics accent aimed at better understanding of the differences in the acoustic features to enhance ASR performance.Apparently from the previous studies reviewed above, it is evident that accent constitute a hurdle to the performance of ASR. Hence, consequently serves as a barrier to ASR wide reception and usage in real life situations. Consequently, it becomes pertinent that accent should be given adequate research attention with the view of enhancing ASR performance to accented speech which will inherently promotes its wide acceptability and applicability globally.
format Conference or Workshop Item
author Atanda, Abdulwahab F.
Mohd Yusof, Shahrul Azmi
Husni, Husniza
author_facet Atanda, Abdulwahab F.
Mohd Yusof, Shahrul Azmi
Husni, Husniza
author_sort Atanda, Abdulwahab F.
title Accent identification of Malaysian and Nigerian English based on acoustic features
title_short Accent identification of Malaysian and Nigerian English based on acoustic features
title_full Accent identification of Malaysian and Nigerian English based on acoustic features
title_fullStr Accent identification of Malaysian and Nigerian English based on acoustic features
title_full_unstemmed Accent identification of Malaysian and Nigerian English based on acoustic features
title_sort accent identification of malaysian and nigerian english based on acoustic features
publishDate 2017
url http://repo.uum.edu.my/24494/1/SICONSEM%202017%2028%2030.pdf
http://repo.uum.edu.my/24494/
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score 13.154949