TITLE:
Robust Parametric Modeling of Speech in Additive White Gaussian Noise
AUTHORS:
Abdelaziz Trabelsi, Otmane Ait Mohamed, Yves Audet
KEYWORDS:
ARMA Model, Noise Variance, Overdetermined Parametric Evaluation, Singular Value Representation, LMS Technique, Yule-Walker Equations
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.6 No.2,
April
2,
2015
ABSTRACT: In estimating the linear prediction
coefficients for an autoregressive spectral model, the concept of using the
Yule-Walker equations is often invoked. In case of additive white Gaussian
noise (AWGN), a typical parameter compensation method involves using a minimal
set of Yule-Walker equation evaluations and removing a noise variance estimate
from the principal diagonal of the autocorrelation matrix. Due to a potential
over-subtraction of the noise variance, however, this method may not retain the
symmetric Toeplitz structure of the autocorrelation matrix and thereby may not
guarantee a positive-definite matrix estimate. As a result, a significant
decrease in estimation performance may occur. To counteract this problem, a
parametric modelling of speech contaminated by AWGN, assuming that the noise
variance can be estimated, is herein presented. It is shown that by combining a
suitable noise variance estimator with an efficient iterative scheme, a
significant improvement in modelling performance can be achieved. The noise
variance is estimated from the least squares analysis of an overdetermined set
of p lower-order Yule-Walker equations. Simulation results indicate that the
proposed method provides better parameter estimates in comparison to the
standard Least Mean Squares (LMS) technique which uses a minimal set of
evaluations for determining the spectral parameters.