研究表明超高斯分布更加贴近语音信号的实际分布，然而语音信号很难用单一的概率密度

函数准确描述，针对这一情况，提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法，并推导了

基于此模型的幅度谱最小均方误差估的估计式。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。 -Recent research indicates that the speech spectral amplitude distributions could

be fairly described with super-Gaussian probability density function. However, the complexity

of speech signal determines that the distribution statistics ofspeech signal could not be well

described by single simple function. Thus a super-Gaussian mixture model for speech spectral

amplitude is proposed, and with this model, a minimum mean-square error (MMSE) estimator for speech

signals spectral amplitude is derived. The simulation results show that this algorithm based on

Gaussian and super-Gaussian speech model could achieve better noise suppression and lower speech

distortion as compared with the conventional short-time spectral amplitude estimation algorithm.