Speech recognition performance deteriorates drastically when they are deployed in practical situation where the speech is corrupted by additive noise. One way to improve the robustness of the speech recognition system is to enhance the speech prior to its recognition. This paper focuses on de-veloping a masking-based β-order minimum mean square error (β-masking MMSE) speech enhancement for speech recognition under noise condition. Addressing the artifacts in-troduced by enhancement algorithm and the remaining noise after denoising, we modified the estimation algorithm of spec-tral parameters for the β-masking MMSE by controlling the power of processing noise, strengthening the weak signal pro-cessing, oversuppressing the residual noise and reestimating a priori SNR. The evaluation shows the proposed enhancement scheme is significantly effective to improve the performance of state-of-the-art speech recognition.