The accuracy of speech recognition system decreases when used on a noisy speech. Therefore, the speech recognition system needs to be supported by a speech enhancement method. This study proposes Berauti spectral subtraction method that uses gaussian window and minimum statistics noise estimation in order to improve the quality of noisy speech hence increase the accuracy of noisy speech recognition. Speech recognition system is built using the Hidden Markov Model Toolkit (HTK). This study applied three types of noise, five SNR levels, six oversubtraction values and four sidelobe gaussian window attenuation values with 1500 speech signals. Improvement of speech recognition accuracy using Gaussian window is compared with Hamming window. The results of the study shows that sidelobe and oversubtraction attenuation values affects recognition accuracy. The average speech recognition accuracy using gaussian window improve about 36.4% which is obtained at oversubtraction 4.75 and sidelobe attenuation = 1.5. Whereas, application of hamming window improves the accuracy about 18,7 % which is obtained at oversubtraction 2.5. Spectral subtraction using gaussian window or hamming window is able to improve the speech recognition accuracy, but gaussian window is better than hamming window
