  A novel feature, based on the chirp z-transform, that offers an improved
representation of the underlying true spectrum is proposed. This feature, the
chirp MFCC, is derived by computing the Mel frequency cepstral coefficients
from the chirp magnitude spectrum, instead of the Fourier transform magnitude
spectrum. The theoretical foundations for the proposal, and the experimental
validation using product of likelihood Gaussians, to show the improved class
separation offered by the proposed chirp MFCC, when compared with vanilla MFCC
are discussed. Further, real world evaluation of the feature is performed using
three diverse tasks, namely, speech-music classification, speaker
identification, and speech commands recognition. It is shown in all three tasks
that the proposed chirp MFCC offers considerable improvements.
