Robust estimation of AR parameters and its application for speech enhancement
Ki Yong Lee, Byung-Gook Lee, Iickho Song, Souguil Ann · 1992
There are two major problems in estimating vocal tract characteristics by conventional linear prediction: estimation accuracy being subject to the characteristics of the excitation source, and the output quality and the estimation accuracy deteriorating with additive background noise. The authors solved these problems as follows: first, estimate the parameters of a robust AR model where the driving noise is a mixture of a Gaussian and an outlier process; then, propose an iterative procedure that involves parameter estimation for uncorrupted speech and data cleaning based on the robust Kalman filter; lastly, the above results are used to enhance speech corrupted by white noise. The results are more efficient and less biased for uncorrupted speech, and superior at low SNR for noisy speech.>