Speech enhancement using super-Gaussian speech models and noncausal a priori SNR estimation

A priori signal-to-noise ratio (SNR) estimation is of major consequence in speech enhancement applications. Recently, we introduced a noncausal recursive estimator for the a priori SNR based on a Gaussian speech model, and showed its advantage compared to using the decision-directed estimator. In particular, noncausal estimation facilitates a distinction between speech onsets and noise irregularities. In this paper, we extend our noncausal estimation approach to Gamma and Laplacian speech models. We show that the performance of noncausal estimation, when applied to the problem of speech enhancement, is better under a Laplacian model than under Gaussian or Gamma models. Furthermore, the choice of the specific speech model has a smaller effect on the enhanced speech signal when using the noncausal a priori SNR estimator than when using the decision-directed method.