MMSE speech enhancement under speech presence uncertainty assuming (generalized) gamma speech priors throughout

摘要

Several investigations showed that speech enhancement approaches can be improved by speech presence uncertainty (SPU) estimation. Although there has been a strong focus on the use of correct statistical models for spectral weighting rules for the last few decades, there is just a few publications about SPU estimation based on a speech prior consistent with the spectral weighting rule. This contribution presents a new consistent solution for MMSE speech amplitude (SA) estimation under SPU, being based on the generalized gamma distribution representing a variety of speech priors. Employing the gamma speech model which is a special case of the generalized gamma distribution, the new approach is shown to outperform both the SPU-based MMSE-SA estimator relying on a Gaussian speech prior, and the gamma MMSE-SA estimation without SPU.