개선된 MFCC와 가우시안 잡음 편차 추정을 이용한 잡음 제거
Noise Elimination Using Improved MFCC and Gaussian Noise Deviation Estimation
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초록
음성 인식 시스템의 지속적인 발전으로 음성에 대한 인식율은 급속도로 발전되었지만 사용 환경에서의 잡음과 여러 음성이 혼합되어 발생하는 잡음으로 정확한 음성을 인식할 수 없는 단점을 가진다. 환경 잡음이 있는 음성을 처리할 때 음성 인식률을 높이기 위해서는 잡음을 제거해야 하며, 기존의 HMM, CHMM, GMM, 그리고 AI 모델이 적용된 DNN에서도 예상치 못한 잡음이 발생하거나 기본적으로 디지털 신호에 양자화 잡음이 추가되면 소스 신호가 변경되거나 손상되어 인식률이 저하된다. 이를 해결하기 위해 각 음성 프레임에 대한 음성 신호의 특징을 효율적으로 추출하기 위해MFCC를 개선하여 처리하였으며, 음성 신호에 대한 잡음을 제거하기 위해 가우시안 모델을 적용한잡음 편차 추정을 이용한 잡음 제거 방법을 개선하여 적용하였다. 제안된 모델에 대한 성능 평가는음성에 대한 정확성 평가를 위해 교차 상관 계수를 사용하여 처리하였으며, 제안하는 방법의 인식률을 평가한 결과 이들에 대한 상관 계수에 대한 평균값 차이는 0.53 dB 개선된 것을 확인하였다.
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.
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