Iterative Noise-Compensated Method to Improve LPC Based Speech Analysis

It is well known that linear predictive coding (LPC) performs well when the prediction coefficients are estimated from noise-free speech, and the system tends to degrade and perform poorly on noisy speech. This paper describes a method to minimize the degradation on the prediction coefficients in the presence of noise when an LPC analysis is used. In this method, a more accurate estimation of noise power is computed by using a simplified noise power spectral density (PSD) estimator. After an inverse discrete Fourier transform (DFT), the extracted noise autocorrelation coefficients are gradually subtracted from the coefficients derived from noisy speech according to an iterative processing scheme. The proposed processing scheme takes the absolute value of the estimated reflection coefficients as the decision criterion. It is shown that performing this iterative procedure on every autocorrelation lag ensures a substantial decrease in the degrading effects of noise, while the estimated autocorrelation matrix is guaranteed to be positive-definite. Experimental results indicate that the variance of the estimated prediction coefficients can be decreased significantly using the proposed method.