On the limitations of cepstral features in noise

This paper examines the effects of additive Gaussian noise on the short-term cepstral analysis of speech. We identify three distinct modifications to the long-term statistics of the cepstrum that cause a gross mismatch after the addition of noise, namely: a mean shift, a change of variance and a distribution distorted from normal, with distinct bimodal characteristics. We assess the importance of each of these, and demonstrate the limitations of simple cepstral mappings. We then consider noise-masking, through the addition of a constant offset to the linear spectral estimates, which provides a feature space far more stable to changes in noise statistics. This leads to performance equivalent to that achieved by explicit modelling