Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

TL;DR

This paper introduces recurrence and fractal scaling analysis tools that effectively detect voice disorders by capturing complex nonlinear and non-Gaussian features, achieving high classification accuracy across diverse voice conditions.

Contribution

The paper presents novel recurrence and fractal scaling measures that directly quantify nonlinear aperiodicity and randomness in voice signals, surpassing limitations of existing methods.

Findings

Achieved 91.8% accuracy in classifying disordered vs. normal voices.

Tools effectively analyze a wide range of voice disorder phenomena.

Applicable to diverse clinical voice assessment scenarios.

Abstract

Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for inherent biophysical nonlinearity and non-Gaussian randomness. They do not directly measure complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools have limited clinical usefulness. This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, and a simple bootstrapped classifier distinguishes normal from disordered voices to 91.8% overall accuracy on a large database of subjects with a wide variety of voice disorders. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.