Analysis of vocal disorders in a feature space

This paper provides a way to classify vocal disorders for clinical applications, thanks to the idea of geometric signal separation in a feature space. It is well known that the human voice source generates complex signals including subharmonics and toroidal oscillations. Typical chaotic quantities — like the entropy and the dimension of the attractor — together with autocorrelation function, power spectrum and other conventional measures are analysed in order to provide entries for the feature vectors. We report on a successful application of the geometrical signal separation in distinguishing between normal and disordered phonation. Both qualitative and quantitative results are presented.