Voice signals contain rich information about human health, and pathological conditions often manifest as distinctive distortions in vocal features. This paper explores acoustic and signal-based characteristics of pathological voice distortions, identifying which features provide the most discriminative power for detecting and classifying vocal pathologies. The study emphasizes the importance of robust feature extraction for developing human–machine systems that support medical diagnostics and continuous health monitoring. Results suggest that carefully selected features can improve classification performance, paving the way for practical applications in computer-aided voice pathology detection.

(DOI not yet available — paper in press)