Time-varying AR speech analysis using robust RLS algorithm with variable forgetting factor

In this paper a new robust recursive method of estimating the linear prediction (LP) parameters of an auto-regressive (AR) speech signal model using weighted least squares (WLS) with variable forgetting factors (VFFs) is described. The proposed robust recursive least squares (RRLS) differs from the conventional recursive least squares (RLS) by the insertion of a suitable chosen nonlinear transformation of the prediction residuals. The RRLS algorithm takes into account the contaminated Gaussian nature of the excitation for voiced speech. In addition, VFF is adapted to a nonstationary speech signal by a generalized likelihood ratio (MGLR) algorithm, which accounts for the nonstationarity of a speech signal. The proposed method has a good adaptability to the nonstationary parts of a speech signal, and gives low bias and low variance at the stationary signal segments.