In this work, we present comparative evaluation of the practical value of some recently proposed speech parameterizations on the speech recognition task. Specifically, in a common experimental setup we evaluate recent discrete wavelet-packet transform (DWPT)-based speech features against traditional techniques, such as the Mel-frequency cepstral coefficients (MFCC) and perceptual linear predictive (PLP) cepstral coefficients that presently dominate the speech recognition field. The relative ranking of eleven sets of speech features is presented.