SPEECH ENHANCEMENT USING A MINIMUM MEAN-SQUARE ERROR SHORT-TIME SPECTRAL AMPLITUDE ESTIMATOR
SPEECH ENHANCEMENT USING A MINIMUM MEAN-SQUARE ERROR SHORT-TIME SPECTRAL AMPLITUDE ESTIMATOR
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DOI：
10.1109/tassp.1984.1164453
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发表时间：
1984-01-01
期刊：
影响因子：
--
通讯作者：
MALAH, D
中科院分区：
文献类型：
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作者：
EPHRAIM, Y;MALAH, D
本文主要研究利用语音信号的短时谱幅度（STSA）在语音感知中的重要性的语音增强系统。提出了一种利用最小均方误差（MMSE）STSA估计器的系统，然后与其他广泛使用的系统进行了比较，这些系统是基于维纳滤波和“谱减法”算法。在本文中，我们推导出MMSE STSA估计，建模语音和噪声频谱分量作为统计独立的高斯随机变量的基础上。我们分析了所提出的STSA估计的性能，并将其与来自维纳估计的STSA估计进行比较。我们还研究了MMSE STSA估计的不确定性下的信号存在的噪声观测。在构造增强信号时，MMSE STSA估计器与噪声相位的复指数相结合。这里表明，后者是原始相位的复指数的MMSE估计器，这不影响STSA估计。所提出的方法的结果在一个显着减少的噪声，并提供增强的语音与无色残留噪声。所提出的算法的复杂性近似于所讨论的类中的其他系统的复杂性。
This paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception. A system which utilizes a minimum mean-square error (MMSE) STSA estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the "spectral subtraction" algorithm. In this paper we derive the MMSE STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables. We analyze the performance of the proposed STSA estimator and compare it with a STSA estimator derived from the Wiener estimator. We also examine the MMSE STSA estimator under uncertainty of signal presence in the noisy observations. In constructing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase. It is shown here that the latter is the MMSE estimator of the complex exponential of the original phase, which does not affect the STSA estimation. The proposed approach results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise. The complexity of the proposed algorithm is approximately that of other systems in the discussed class.