  Source-tract decomposition (or glottal flow estimation) is one of the basic
problems of speech processing. For this, several techniques have been proposed
in the literature. However studies comparing different approaches are almost
nonexistent. Besides, experiments have been systematically performed either on
synthetic speech or on sustained vowels. In this study we compare three of the
main representative state-of-the-art methods of glottal flow estimation:
closed-phase inverse filtering, iterative and adaptive inverse filtering, and
mixed-phase decomposition. These techniques are first submitted to an objective
assessment test on synthetic speech signals. Their sensitivity to various
factors affecting the estimation quality, as well as their robustness to noise
are studied. In a second experiment, their ability to label voice quality
(tensed, modal, soft) is studied on a large corpus of real connected speech. It
is shown that changes of voice quality are reflected by significant
modifications in glottal feature distributions. Techniques based on the
mixed-phase decomposition and on a closed-phase inverse filtering process turn
out to give the best results on both clean synthetic and real speech signals.
On the other hand, iterative and adaptive inverse filtering is recommended in
noisy environments for its high robustness.
