A comparative performance evaluation of adaptive ARMA spectral estimation methods for noisy speech

Abstract

The problem of adaptive estimation of linear prediction (LP) coefficients from noisy speech is considered. Performance of three adaptive ARMA spectral estimation algorithms are studied for this purpose: the recursive extended least squares (RELS) algorithm, the recursive maximum likelihood (RML) algorithm, and the overdetermined recursive instrumental variable (ORIV) algorithm. To put them in proper perspective, the normalized LMS (NLMS) has also been considered. The ORIV algorithm is found to be the best in terms of Itakura distance from the ideal LP coefficients and the power spectral density estimation. The RML algorithm is found to be robust in highly noisy cases.<>