Speech Enhancement Using Minimum Mean-Square Error Amplitude Estimators Under Normal and Generalized Gamma Distribution

Abstract

Problem statement: In this study, DFT-based speech enhancement via Minimum Mean-Square Error (MMSE) amplitude estimators was considered. Approach: Several variants of the basic approach (MMSE-STSA) have been proposed over the years to address certain shortcomings, chiefly the quality of the remnant noise and its trade-off with speech distortion. In this study, we presented a comparative study between the MMLSA and the estimators based on the Gamma model, followed by an implementation in Matlab of these algorithms and an objective evaluation using a corpus of speech. Results: We obtained the best values of various parameters used by different estimators. Conclusion: Objective evaluation confirm superiority in noise suppression and quality of the enhanced speech by the estimators derived under the generalized Gamma distribution than the estimators derived under the normal distribution, in stationary environments.