“…A significant number of noise-robustness techniques have been proposed to address the noise problem, and one prevailing subset of these techniques is focused on reducing the statistical mismatch of speech features in the training and testing conditions of the recognizer. Typical examples are perceptual masking [1], empirical mode decomposition [2], optimally modified log-spectral amplitude estimation [3], wavelet packet decomposition with AR modeling [4], cepstral mean and variance normalization (MVN) [5], cepstral histogram normalization (CHN) [6,7], MVN with ARMA filtering (MVA) [8], higher order cepstral moment normalization (HOCMN) [9], and temporal structure normalization (TSN) [10]. In some of these methods, the compensation is performed on each individual cepstral channel sequence of an utterance by assuming that these channels are mostly uncorrelated [7].…”