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We consider DFT based techniques for single-channel speech enhancement. Specifically, we derive minimum mean-square error estimators of clean speech DFT coefficients based on generalized gamma prior probability density functions. Our estimators contain as special cases the well-known Wiener estimator and the more recently derived estimators based on Laplacian and two-sided gamma priors. Simulation experiments with speech signals degraded by various additive noise sources verify that the estimator based on the two-sided gamma prior is close to optimal amongst all the estimators considered in this paper.