Robust MFCCs Derived from Differentiated Power Spectrum

The mel-scaled frequency cepstral coefficients (MFCCs) derived from Fourier transform and filter bank analysis are perhaps the most widely used front-ends in state-of-the-art speech recognition systems. One of the major issues with the MFCCs is that they are very sensitive to additive noise. To improve the robustness of speech front-ends with respect to noise, we introduce, in this paper, a new set of MFCC vector which is estimated through three steps. First, the power spectrum of speech signal is estimated through the fast Fourier transform (FFT). Then the power spectrum is differentiated with respected to frequency. Finally, the differentiated power spectrum is transformed into MFCC-like coefficients. Speech recognition experiments for various tasks indicate that the new feature vector is more robust than traditional mel-scaled frequency cepstral coefficients (MFCCs) in additive noise conditions.