Estimation of LPC coefficients from speech waveforms degraded by additive random noise

Application of a Maximum A Posteriori (MAP) estimation procedure in estimating the LPC coefficients from speech waveforms degraded by additive random noise generally leads to solving a set of non-linear equations which are computationally undesirable. However, an attempt to approximate the true MAP estimation procedure leads to two iterative methods that require solving only sets of linear equations. These two methods have been applied to real speech data degraded by additive white Gaussian noise, and in this paper some preliminary results are discussed.