Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation (개선된 MFCC와 가우시안 잡음 편차 추정을 이용한 잡음 제거)

Sang-Yeob Oh (오상엽)

doi:10.9708/jksci.2023.28.01.087

Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2023, 28(1), pp.87-92
DOI : 10.9708/jksci.2023.28.01.087
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 4, 2022
Accepted : October 20, 2022
Published : January 31, 2023

Sang-Yeob Oh ¹

¹가천대학교

Accredited

ABSTRACT

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.

KEYWORDS

Voice recognition, noise, MFCC, gaussian model, feature extraction

Citation status

* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation

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KEYWORDS

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