  This paper provides a way to classify vocal disorders for clinical
applications. This goal is achieved by means of geometric signal separation in
a feature space. Typical quantities from chaos theory (like entropy,
correlation dimension and first lyapunov exponent) and some conventional ones
(like autocorrelation and spectral factor) are analysed and evaluated, in order
to provide entries for the feature vectors. A way of quantifying the amount of
disorder is proposed by means of an healthy index that measures the distance of
a voice sample from the centre of mass of both healthy and sick clusters in the
feature space. A successful application of the geometrical signal separation is
reported, concerning distinction between normal and disordered phonation.
