Speech enhancement based on spectral estimation from higher-lag autocorrelation

In this paper, we propose a unique approach to enhance speech signals that have been corrupted by non-stationary noises. This approach is not based on a spectral subtraction algorithm, but on an algorithm that separates the speech signal and noise signal contributions in the autocorrelation domain. We call this technique the AR-HASE speech enhancement algorithm. In this initial study, we evaluate the performance of the new algorithm using the average PESQ score computed from 10 male utterances and 10 female utterances taken from the TIMIT database as a measure of speech quality. We test the algorithm using one broadband stationary noise and two non-stationary noises. We will show that the AR-HASE enhancement algorithm produces near transparent quality for clean speech, gives poor enhancement performance for broadband stationary noises, and gives significantly enhanced quality for the two non-stationary noises.