Machine Learning-based Study of Dysphonic Voices for the Identification and Differentiation of Vocal Cord Paralysis and Vocal Nodules

Dysphonia can be caused by multiple different conditions, which are often indistinguishable through perceptual evaluation, even when undertaken by experienced clinicians. Furthermore, definitive diagnoses are often not immediate and performed only in clinical settings through laryngoscopy, which is an invasive procedure. This study took into account Vocal Cord Paralysis (VCP) and Vocal Nodules (VN) given their perceptual similarity and, with the aid of euphonic control subjects, aimed to build a framework for the identification and differentiation of the diseases. A dataset of voice recordings comprised of 87 control subjects, 85 subjects affected by VN, and 120 subjects affected by VCP was carefully built within a controlled clinical setting. A Machine-Learning framework was built, based on a correlation-based feature selection bringing relevant biomarkers, followed by a ranker and a Gaussian Support Vector Machine (SVM) classifier. The results of the classifications were promising, with the comparisons versus healthy subjects bringing accuracies higher than 98%, while 89.21% was achieved for the differentiation. This suggests that it may be possible to automatically identify dysphonic voices, differentiating etiologies of dysphonia. The selected biomarkers further validate the analysis highlighting a trend of poor volume control in dysphonic subjects, while also refining the existing literature.