A new speech enhancement algorithm with generalized Gamma speech model
Gaihua Zhao, Bin Zhou, Xiongwei Zhang, Sui Lu-ying · 2012
In this paper, we present a new speech enhancement algorithm based on generalized Gamma speech model, which is more flexible in capturing the statistical behavior of speech signals than the conventional Gaussian and super-Gaussian speech model. Under the assumption of a generalized Gamma distribution for the clean speech spectral amplitudes and additive Gaussian noise, we derive a minimum mean-square error (MMSE) estimator of the log-spectra amplitude for speech signals. Furthermore, the speech presence probability is consistent with the new model which is derived to modify the MMSE estimator. The experimental results show that the proposed algorithm yields improvements in segmental signal-to-noise ratio (SSNR), less residual noise and better perception in speech quality, compared to the conventional short-time spectral amplitude estimators, which are based on Gaussian and super-Gaussian speech model.