A time-warping framework for speech turbulence-noise component estimation during aperiodic phonation

The accurate estimation of turbulence noise affects many areas of speech processing including separate modification of the noise component, analysis of degree of speech aspiration for treating pathological voice, the automatic labeling of speech voicing, as well as speaker characterization and recognition. Previous work in the literature has provided methods by which such a high-quality noise component may be estimated in near-periodic speech, but it is known that these methods tend to leak aperiodic phonation (with even slight deviations from periodicity) into the noise-component estimate. In this paper, we improve upon existing algorithms in conditions of aperiodicity by introducing a time-warping based approach to speech noise-component estimation, demonstrating the results on both natural and synthetic speech examples.