This provides a popular alternative method of deriving cepstral coefficients. LPC has problems with certain signal degradations and is not so convenient for producing mel-scale cepstral coefficients. Many systems augment information on the short-term power spectrum with information on its rate of change over time. The simplest way to obtain this dynamic information would be to take the difference between consecutive frames. However, this turns out to be too sensitive to random interframe variations.
Some systems go further and estimate acceleration features as well as linear rates of change.
Since they are based on differences, however, dynamic features are intrinsically immune to such constant effects. If first-order dynamic parameters are passed through a leaky integrator , something close to the original static parameters are recovered except that constant and very slowly varying features are reduced to zero, thus giving independence from constant or slowly varying channel characteristics. Because cepstral coefficients are largely uncorrelated, a computationally efficient method of obtaining reasonably good probability estimates in the subsequent matching process consists of calculating Euclidean distances from reference model vectors after suitably weighting the coefficients. Various weighting schemes have been used. The vast majority of major commercial and experimental systems use representations akin to those described here.