the logarithmic which is usually used for MFCC computation. Furthermore, the method is easily modified to take into account other compressive non-linearities than. The strength of the proposed method is that it allows MMSE estimation of mel-frequency cepstral coefficients (MFCC's), cepstral mean-subtracted MFCC's (CMS-MFCC's), velocity, and acceleration coefficients. The method is based on a minimum number of well-established statistical assumptions no assumptions are made which are inconsistent with others. We propose a method for minimum mean-square error (MMSE) estimation of mel-frequency cepstral features for noise robust automatic speech recognition (ASR). A Theoretically Consistent Method for Minimum Mean-Square Error Estimation of Mel-Frequency Cepstral Features
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