Minimum Mean Square Error estimation of speech spectral amplitude using super-Gaussian priors

This paper deals with the spectral estimation methods for speech enhancement. We firstly show the proper matching of the super-Gaussian distribution with the histogram of the speech spectral amplitude. For the selected speech material, the best matching is achieved when the super-Gaussian parameters are set to ν = 0, μ = 2.5. We then derive Minimum Mean Square Error (MMSE) estimator for speech DFT amplitude when clean speech spectral amplitudes are modeled by super-Gaussian probability distribution and noise DFT coefficients are presented as Gaussian random variables. Evaluation results, in terms of different objective quality measures, show that the MMSE estimator based on super-Gaussian distribution (with parameters ν = 0, μ = 2.5) leads to superior results in speech enhancement.