Analysis of Vocal Disorders in a Feature Space

Details Analysis of Vocal Disorders in a Feature Space Analysis of Vocal Disorders in a Feature Space

2000-09-13

arxiv.org/abs/cond-mat/0009188v1

Physics

Condensed Matter

12 pages, 3 figures, accepted for publication in Medical Engineering & Physics

Scientific paper

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.

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