@article{ART002927636},
author={Sang-Yeob Oh},
title={Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={1},
pages={87-92},
doi={10.9708/jksci.2023.28.01.087}

TY - JOUR
AU - Sang-Yeob Oh
TI - Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 1
PB - The Korean Society Of Computer And Information
SP - 87
EP - 92
SN - 1598-849X
AB - With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.
KW - Voice recognition;noise;MFCC;gaussian model;feature extraction
DO - 10.9708/jksci.2023.28.01.087
ER -

Sang-Yeob Oh. (2023). Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation. Journal of The Korea Society of Computer and Information, 28(1), 87-92.

Sang-Yeob Oh. 2023, "Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation", Journal of The Korea Society of Computer and Information, vol.28, no.1 pp.87-92. Available from: doi:10.9708/jksci.2023.28.01.087

Sang-Yeob Oh "Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation" Journal of The Korea Society of Computer and Information 28.1 pp.87-92 (2023) : 87.

Sang-Yeob Oh. Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation. 2023; 28(1), 87-92. Available from: doi:10.9708/jksci.2023.28.01.087

Sang-Yeob Oh. "Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation" Journal of The Korea Society of Computer and Information 28, no.1 (2023) : 87-92.doi: 10.9708/jksci.2023.28.01.087

Sang-Yeob Oh. Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation. Journal of The Korea Society of Computer and Information, 28(1), 87-92. doi: 10.9708/jksci.2023.28.01.087

Sang-Yeob Oh. Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation. Journal of The Korea Society of Computer and Information. 2023; 28(1) 87-92. doi: 10.9708/jksci.2023.28.01.087

Sang-Yeob Oh. Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation. 2023; 28(1), 87-92. Available from: doi:10.9708/jksci.2023.28.01.087

Sang-Yeob Oh. "Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation" Journal of The Korea Society of Computer and Information 28, no.1 (2023) : 87-92.doi: 10.9708/jksci.2023.28.01.087