A Model-Based Spectral Envelope Wiener Filter for Perceptually Motivated Speech Enhancement

In this work, we present a model-based Wiener filter whose frequency response is optimized in the dimensionally reduced log-Mel domain. That is achieved by making use of a reasonably novel speech feature enhancement approach that has originally been developed in the area of speech recognition. Its combination with Wiener filtering is motivated by the fact that signal reconstruction from log-Mel features sounds very unnatural. Hence, we correct only the spectral envelope and preserve the fine spectral structure of the noisy signal. Experiments on a Wall Street Journal corpus showed a relative improvement of up to 24% relative in PESQ and 45% relative in log spectral distance (LSD), compared to Ephraim and Mallah's log spectral amplitude estimator.