Comparative Evaluation of the Performance of Vocal Signals and EGG in the Classification of Vocal Pathologies

Abstract

This study proposes a methodology for the classification of vocal pathologies by comparing voice signals with electroglottographic (EGG) signals. The segmentation of the voice signal into temporal components and its transformation into recurrence plots through intuitionistic fuzzy clustering provides input for a deep learning model to classify voices as healthy or pathological. The results obtained show that the Inception-v3 model, when using intuitionistic clustering, achieves superior accuracy — particularly with EGG signals — reaching a peak performance of 87.8%. Furthermore, the F1 score is 0.885 for EGG and 0.860 for speech, demonstrating better performance on EGG signals.