A Mask-Based Post Processing Approach for Improving the Quality and Intelligibility of Deep Neural Network Enhanced Speech

Abstract

In this paper, we propose a method for post-processing of deep neural network (DNN) enhanced speech. The method, which is simple and does not require additional training or expansion of the feature or target vectors, can be viewed as a mask-based approach in which a noisy speech signal is processed by a time-frequency (T-F) weighting derived from the noise-free spectral estimate of a DNN. A series of experiments and statistical analyses of results are carried out to compare the performance of the proposed approach to a baseline DNN enhancement system that features no post processing. Objective tests show that the proposed approach always improves both speech quality and intelligibility, and it outperforms a corresponding baseline system in both matched and mismatched noise conditions. Analysis of the enhanced speech shows that post processing reduces severe amplification distortions in the magnitude spectrum of the enhanced speech at the cost of a slight increase in severe attenuation distortions.