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In this paper, a novel speech enhancement algorithm is proposed. The algorithm controls the amount of noise reduction according to whether speech absence or presence in noisy environments. Based on the estimated speech absence probability (SAP), the amount of noise reduction is adaptively controlled. To calculate the SAP, normalized cross correlation of linear predictive residual signals instead of that of original input signals is utilized. It is especially robust and effective in reverberant and realistic environments. Experimental results show that the proposed algorithm improves speech recognition rates compared with conventional linearly constraint minimum variance beamforming.