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Updated: Aug 6, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
Published on: September 27, 2024
Analysis of vocal disorders in a feature space
L Matassini1, R Hegger, H Kantz
1Max-Planck-Institut für Physik komplexer Systeme, Nöthnitzer Str. 38, D 01187, Dresden, Germany. lorenzo@mpipks-dresden.mpg.de
Abstract:
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 a 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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