Speech enhancement minimizing generalized euclidean distortion using supergaussian priors

We introduce short time spectral estimators which minimize the weighted Euclidean distortion (WED) between the clean and estimated speech spectral components when clean speech is degraded by additive noise. The traditional minimummean square error (MMSE) estimator does not take into account sufficient perceptual measure during enhancement of noisy speech. However, the new estimators discussed in this paper provide greater flexibility to improve speech quality. We explore the cases when clean speech spectral magnitude and discrete Fourier transform (DFT) coefficients are modeled by super-Gaussian priors like Chi and bilateral Gamma distributions respectively. We also present the joint maximum a posteriori (MAP) estimators of the Chi distributed spectral magnitude and uniform phase. Performance evaluations over two noise types and three SNR levels demonstrate improved results of the proposed estimators.