修正的基于广义Gamma语音模型语音增强算法

广义Gamma模型是近年来新提出的一种语音分布模型，相对于传统的高斯或超高斯模型具有更好的普适性和灵活性，提出一种基于广义Gamma语音模型和语音存在概率修正的语音增强算法。在假设语音和噪声的幅度谱系数分别服从广义Gamma分布和Gaussian分布的基础上，推导了语音信号对数谱的最小均方误差估计式；在该模型下进一步推导了语音存在概率，对最小均方误差估计进行修正。仿真结果表明，与传统的短时谱估计算法相比，该算法不仅能够进一步提高增强语音的信噪比，而且可以有效减小增强语音的失真度，提高增强语音的主观感知质量。

Modified speech enhancement algorithm under signal presence probability with generalized Gamma speech model

This paper presents a modified speech enhancement algorithm under signal presence probability. Generalized Gamma distribution priors are assumed for speech short-time spectral amplitudes, which is more flexible in capturing the statistical behavior of speech signals. It derives a Minimum Mean-Square Error（MMSE）estimator of the log-spectra am-plitude for speech signals, under the assumption of a generalized Gamma speech priors and additive Gaussian noise priors. Furthermore, modification under signal presence probability is obtained, which is estimated for each frequency bin and each frame consistent with the new model. The simulation results show that the proposed algorithm achieves better noise suppression and lower speech distortion compared to the conventional short-time spectral amplitude estimators, which are based on Gaussian and super-Gaussian speech model.