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SPEAKER IDENTIFICATION MENGGUNAKAN TRANSFORMASI WAVELET DISKRIT DAN JARINGAN SARAF TIRUAN BACK-PROPAGATION

Main Authors: Tandyo, Anny, Martono, Martono, Widyatmoko, Adi
Format: Article info application/pdf
Bahasa: eng
Terbitan: Bina Nusantara University , 2008
Online Access: https://journal.binus.ac.id/index.php/commit/article/view/482
https://journal.binus.ac.id/index.php/commit/article/view/482/460
ctrlnum article-482
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">SPEAKER IDENTIFICATION MENGGUNAKAN TRANSFORMASI WAVELET DISKRIT DAN JARINGAN SARAF TIRUAN BACK-PROPAGATION</title><creator>Tandyo, Anny</creator><creator>Martono, Martono</creator><creator>Widyatmoko, Adi</creator><description lang="en-US">Article discussed a speaker identification system. Which was a part of speaker recognition. The system identified asubject based on the voice from a group of pattern had been saved before. This system used a wavelet discrete transformationas a feature extraction method and an artificial neural network of back-propagation as a classification method. The voiceinput was processed by the wavelet discrete transformation in order to obtain signal coefficient of low frequency as adecomposition result which kept voice characteristic of everyone. The coefficient then was classified artificial neural networkof back-propagation. A system trial was conducted by collecting voice samples directly by using 225 microphones in nonsoundproof rooms; contained of 15 subjects (persons) and each of them had 15 voice samples. The 10 samples were used as atraining voice and 5 others as a testing voice. Identification accuracy rate reached 84 percent. The testing was also done onthe subjects who pronounced same words. It can be concluded that, the similar selection of words by different subjects has noinfluence on the accuracy rate produced by system.Keywords: speaker identification, wavelet discrete transformation, artificial neural network, back-propagation.</description><publisher lang="en-US">Bina Nusantara University</publisher><date>2008-05-31</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>https://journal.binus.ac.id/index.php/commit/article/view/482</identifier><identifier>10.21512/commit.v2i1.482</identifier><source lang="en-US">CommIT (Communication and Information Technology) Journal; Vol. 2 No. 1 (2008): CommIT Journal; 1-7</source><source>2460-7010</source><source>1979-2484</source><language>eng</language><relation>https://journal.binus.ac.id/index.php/commit/article/view/482/460</relation><recordID>article-482</recordID></dc>
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author Tandyo, Anny
Martono, Martono
Widyatmoko, Adi
title SPEAKER IDENTIFICATION MENGGUNAKAN TRANSFORMASI WAVELET DISKRIT DAN JARINGAN SARAF TIRUAN BACK-PROPAGATION
publisher Bina Nusantara University
publishDate 2008
url https://journal.binus.ac.id/index.php/commit/article/view/482
https://journal.binus.ac.id/index.php/commit/article/view/482/460
contents Article discussed a speaker identification system. Which was a part of speaker recognition. The system identified asubject based on the voice from a group of pattern had been saved before. This system used a wavelet discrete transformationas a feature extraction method and an artificial neural network of back-propagation as a classification method. The voiceinput was processed by the wavelet discrete transformation in order to obtain signal coefficient of low frequency as adecomposition result which kept voice characteristic of everyone. The coefficient then was classified artificial neural networkof back-propagation. A system trial was conducted by collecting voice samples directly by using 225 microphones in nonsoundproof rooms; contained of 15 subjects (persons) and each of them had 15 voice samples. The 10 samples were used as atraining voice and 5 others as a testing voice. Identification accuracy rate reached 84 percent. The testing was also done onthe subjects who pronounced same words. It can be concluded that, the similar selection of words by different subjects has noinfluence on the accuracy rate produced by system.Keywords: speaker identification, wavelet discrete transformation, artificial neural network, back-propagation.
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