MMSE Log-Spectral Amplitude Estimation Under Speech Presence Uncertainty Using Generalized Γ Speech Priors

Abstract:
MMSE log-spectral amplitude (LSA) estimators offer convincing performance and are, therefore, widely spread in speech enhancement. In this paper an MMSE-LSA estimation under speech presence uncertainty (SPU) with a generalized γ speech prior representing a variety of speech priors is proposed. Employing a γ speech prior as a special case of the generalized approach, a numerical approximation is employed. Different to most state-of-the-art approaches, we choose the pdf assumptions of the mere spectral weighting rule and of the SPU estimation to be consistent. It is shown that the use of the SPU estimator with an underlying γ speech pdf yields more than a 3 dB gain in noise attenuation, with a slight advantage versus the Gaussian SPU estimator.