Improving LPC analysis of noisy speech by autocorrelation subtraction method

Abstract

A robust linear predictive coding (LPC) method that can be used in noisy as well as quiet environment has been studied. In this method, noise autocorrelation coefficients are first obtained and updated during non-speech periods. Then, the effect of additive noise in the input speech is removed by subtracting values of the noise autocorrelation coefficients from those of autocorrelation coefficients of corrupted speech in the course of computation of linear prediction coefficients. When signal-to-noise ratio of the input speech ranges from 0 to 10 dB, a performance improvement of about 5 dB can be gained by using this method. The proposed method is computationally very efficient and requires a small storage area.