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Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation

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@article{IJASEIT1360,
   author = {Abdul Rahim Bahari and Aminatuzzaharah Musa and Mohd Zaki Nuawi and Zairi Ismael Rizman and Suziana Mat Saad},
   title = {Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {4},
   year = {2017},
   pages = {1172--1177},
   keywords = {speech recognition; kurtosis; clustering; skewness; voice signal},
   abstract = {This paper discusses the development method to determine the accuracy of pronunciation of the word using global statistical signal analysis parameters. An engineering word that has been chosen is ‘leaching’. The pronunciation of the word ‘leaching’ in the French language has been recorded from 1 native speaker and 4 students. The recording processes use a microphone-laptop system configuration and the signal analyzing processes use MATLAB software. Time and frequency domain plots show a variety of waveforms according to the recorded pronunciation. For data processing, statistical signal analysis parameters involved to extract the signal’s features are kurtosis, root mean square and skewness. The mapping process has been performed to cluster each data. The position of the samples from the students is referred to the samples from the native speaker. The result of the accuracy of the pronunciation of words for each student can be evaluated through the comparison of the position of all the samples. In conclusion, the development of mapping and clustering methods are able to characterize the accuracy of the pronunciation of words.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1360},
   doi = {10.18517/ijaseit.7.4.1360}
}

EndNote

%A Bahari, Abdul Rahim
%A Musa, Aminatuzzaharah
%A Nuawi, Mohd Zaki
%A Rizman, Zairi Ismael
%A Mat Saad, Suziana
%D 2017
%T Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation
%B 2017
%9 speech recognition; kurtosis; clustering; skewness; voice signal
%! Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation
%K speech recognition; kurtosis; clustering; skewness; voice signal
%X This paper discusses the development method to determine the accuracy of pronunciation of the word using global statistical signal analysis parameters. An engineering word that has been chosen is ‘leaching’. The pronunciation of the word ‘leaching’ in the French language has been recorded from 1 native speaker and 4 students. The recording processes use a microphone-laptop system configuration and the signal analyzing processes use MATLAB software. Time and frequency domain plots show a variety of waveforms according to the recorded pronunciation. For data processing, statistical signal analysis parameters involved to extract the signal’s features are kurtosis, root mean square and skewness. The mapping process has been performed to cluster each data. The position of the samples from the students is referred to the samples from the native speaker. The result of the accuracy of the pronunciation of words for each student can be evaluated through the comparison of the position of all the samples. In conclusion, the development of mapping and clustering methods are able to characterize the accuracy of the pronunciation of words.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1360
%R doi:10.18517/ijaseit.7.4.1360
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 4
%@ 2088-5334

IEEE

Abdul Rahim Bahari,Aminatuzzaharah Musa,Mohd Zaki Nuawi,Zairi Ismael Rizman and Suziana Mat Saad,"Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, pp. 1172-1177, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4.1360.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Bahari, Abdul Rahim
AU  - Musa, Aminatuzzaharah
AU  - Nuawi, Mohd Zaki
AU  - Rizman, Zairi Ismael
AU  - Mat Saad, Suziana
PY  - 2017
TI  - Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4
Y2  - 2017
SP  - 1172
EP  - 1177
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - speech recognition; kurtosis; clustering; skewness; voice signal
N2  - This paper discusses the development method to determine the accuracy of pronunciation of the word using global statistical signal analysis parameters. An engineering word that has been chosen is ‘leaching’. The pronunciation of the word ‘leaching’ in the French language has been recorded from 1 native speaker and 4 students. The recording processes use a microphone-laptop system configuration and the signal analyzing processes use MATLAB software. Time and frequency domain plots show a variety of waveforms according to the recorded pronunciation. For data processing, statistical signal analysis parameters involved to extract the signal’s features are kurtosis, root mean square and skewness. The mapping process has been performed to cluster each data. The position of the samples from the students is referred to the samples from the native speaker. The result of the accuracy of the pronunciation of words for each student can be evaluated through the comparison of the position of all the samples. In conclusion, the development of mapping and clustering methods are able to characterize the accuracy of the pronunciation of words.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1360
DO  - 10.18517/ijaseit.7.4.1360

RefWorks

RT Journal Article
ID 1360
A1 Bahari, Abdul Rahim
A1 Musa, Aminatuzzaharah
A1 Nuawi, Mohd Zaki
A1 Rizman, Zairi Ismael
A1 Mat Saad, Suziana
T1 Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 4
YR 2017
SP 1172
OP 1177
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 speech recognition; kurtosis; clustering; skewness; voice signal
AB This paper discusses the development method to determine the accuracy of pronunciation of the word using global statistical signal analysis parameters. An engineering word that has been chosen is ‘leaching’. The pronunciation of the word ‘leaching’ in the French language has been recorded from 1 native speaker and 4 students. The recording processes use a microphone-laptop system configuration and the signal analyzing processes use MATLAB software. Time and frequency domain plots show a variety of waveforms according to the recorded pronunciation. For data processing, statistical signal analysis parameters involved to extract the signal’s features are kurtosis, root mean square and skewness. The mapping process has been performed to cluster each data. The position of the samples from the students is referred to the samples from the native speaker. The result of the accuracy of the pronunciation of words for each student can be evaluated through the comparison of the position of all the samples. In conclusion, the development of mapping and clustering methods are able to characterize the accuracy of the pronunciation of words.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1360
DO  - 10.18517/ijaseit.7.4.1360