This paper focuses on optimal estimators of the magnitude spectrum for speech enhancement. We present an analytical solution for estimating in the MMSE sense the magnitude spectrum when the clean speech DFT coefficients are modeled by a Laplacian distribution and the noise DFT coefficients are modeled by a Gaussian distribution. Furthermore, we derive the MMSE estimator under speech presence uncertainty and a Laplacian statistical model. Results indicated that the Laplacian-based MMSE estimator yielded less residual noise in the enhanced speech than the traditional Gaussian-based MMSE estimator. Overall, the present study demonstrates that the assumed distribution of the DFT coefficients can have a significant effect on the quality of the enhanced speech.

中文翻译：

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用于语音增强的基于拉普拉斯算子的 MMSE 估计器。

本文重点研究用于语音增强的幅度谱的最佳估计器。当干净的语音 DFT 系数由拉普拉斯分布建模并且噪声 DFT 系数由高斯分布建模时，我们提出了一种解析解，用于在 MMSE 意义上估计幅度谱。此外，我们在语音存在不确定性和拉普拉斯统计模型下推导出 MMSE 估计量。结果表明，与传统的基于高斯的 MMSE 估计器相比，基于 Laplacian 的 MMSE 估计器在增强语音中产生更少的残余噪声。总体而言，本研究表明，假设的 DFT 系数分布会对增强语音的质量产生显着影响。