Classification of Voice Disorders

Abstract

This article introduces the classification of pathological voices, a vital area in vocal health. Our work presents a methodology integrating signal processing, including wavelet analysis and parameter extraction, and unveils promising results achieved using a Support Vector Machine (SVM) model for classification. The primary objective is to enhance the diagnosis and management of vocal disorders by providing more effective tools to healthcare professionals, based on the VOICED database. This work underscores the critical importance of vocal health and the necessity of investing in more precise and accessible diagnostic methods, while also opening up new prospects for care and the quality of life of affected individuals.