  In this paper, we present a method that allows to further improve speech
enhancement obtained with recently introduced Deep Neural Network (DNN) models.
We propose a multi-channel refinement method of time-frequency masks obtained
with single-channel DNNs, which consists of an iterative Complex Gaussian
Mixture Model (CGMM) based algorithm, followed by optimum spatial filtration.
We validate our approach on time-frequency masks estimated with three recent
deep learning models, namely DCUnet, DCCRN, and FullSubNet. We show that our
method with the proposed mask refinement procedure allows to improve the
accuracy of estimated masks, in terms of the Area Under the ROC Curve (AUC)
measure, and as a consequence the overall speech quality of the enhanced speech
signal, as measured by PESQ improvement, and that the improvement is consistent
across all three DNN models.
