GMM based Bayesian approach to speech enhancement in signal / transform domain
Achintya Kundu, Saikat Chatterjee, A. S. Vasudeva Murthy, T.V. Sreenivas · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.