Improving the performance of MFCC for Persian robust speech recognition

The mel frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. in this paper to achieve a satisfactorily performance in automatic speech recognition (asr) applications we introduce a noise robust new set of mfcc vector estimated through following steps. first, spectral mean normalization is a pre-processing which applies to the noisy original speech signal. the pre-emphasized original  speech segmented into overlapping time frames, then it is windowed by a modified hamming window .higher order autocorrelation coefficients are extracted. the next step is to eliminate the lower order of the autocorrelation coefficients. the consequence pass from fft block and then power spectrum of output is calculated. a gaussian shape filter bank is applied to the results. logarithm and two compensator blocks form which one is mean subtraction and the other one are root block applied to the results and dct transformation is the last step. we use mlp neural network to evaluate the performance of proposed mfcc method and to classify the results. some speech recognition experiments for various tasks indicate that the proposed algorithm is more robust than traditional ones in noisy condition.