Metode Wavelet-MFCC dan Korelasi dalam Pengenalan Suara Digit

JTIM : Jurnal Teknologi Informasi dan Multimedia

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Field Value
 
Title Metode Wavelet-MFCC dan Korelasi dalam Pengenalan Suara Digit
 
Creator Dyarbirru, Zaurarista
Hidayat, Syahroni
 
Subject Automatic Speech Recognition
MFCC method
wavelet
wavelet-MFCC
K-Fold Cross Validation
 
Description Voice is the sound emitted from living things. With the development of Automatic Speech Recognition (ASR) technology, voice can be used to make it easier for humans to do something. In the ASR extraction process the features have an important role in the recognition process. The feature extraction methods that are commonly applied to ASR are MFCC and Wavelet. Each of them has advantages and disadvantages. Therefore, this study will combine the wavelet feature extraction method and MFCC to maximize the existing advantages. The proposed method is called Wavelet-MFCC. Voice recognition method that does not use recommendations. Determination of system performance using the Word Recoginition Rate (WRR) method which is validated with the K-Fold Cross Validation with the number of folds is 5. The research dataset used is voice recording digits 0-9 in English. The results show that the digit speech recognition system that has been built gives the highest average value of 63% for digit 4 using wavelet daubechies DB3 and wavelet dyadic transform method. As for the comparison results of the wavelet decomposition method used, that the use of dyadic wavelet transformation is better than the wavelet package.
 
Publisher Puslitbang Sekawan Institute Nusa Tenggara
 
Date 2020-08-21
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://journal.sekawan-org.id/index.php/jtim/article/view/99
10.35746/jtim.v2i2.99
 
Source JTIM : Jurnal Teknologi Informasi dan Multimedia; Vol 2 No 2 (2020): August; 100-108
2684-9151
10.35746/jtim.v2i2
 
Language eng
 
Relation https://journal.sekawan-org.id/index.php/jtim/article/view/99/69
 
Rights Copyright (c) 2020 Zaurarista Dyarbirru, Syahroni Hidayat
https://creativecommons.org/licenses/by-sa/4.0
 

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