Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model
EURASIP Journal on Applied Signal Processing
Minimum Mean-Square Error Estimation of Discrete Fourier Coefficients With Generalized Gamma Priors
IEEE Transactions on Audio, Speech, and Language Processing
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Statistical speech enhancement methods often rely on a set of assumptions, like gaussianity of speech and noise processes or perfect knowledge of their parameters, that are not fully met in reality. Recent advancements have shown the potential improvement in speech enhancement obtained by employing supergaussian speech models conditioned on the estimated signal to noise ratio. In this paper we derive a supergaussian model for speech enhancement in which both speech and noise priors are assumed to be complex Gaussian mixture models. We introduce as well a method for the computation of the noise prior based on the noise variance estimator used. Finally, we compare the developed estimators with the conventional Ephraim-Malah filters in the context of robust automatic speech recognition.