传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这

一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT)

系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表

示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的

语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优

于传统的语音增强方法。-The spectral components of speech are usually assumed to be independent in traditional short-time

spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm

with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform

(DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral

components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed

model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech

presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show

that the developed method has better noise suppression performance and lower speech distortion compared to the

traditional speech enhancement method.