Reinke’s edema diagnosis support system based on voice recordings and machine learning

Abstract

Voice pathologies have a direct impact on human communication. One of the most common voice disorders is Reinke’s edema. Speech analysis algorithms applied to voice recordings in combination with machine learning techniques are explored to develop non-invasive low-cost tools to help diagnose this pathology. Different approaches have been compared to discriminate subjects affected by Reinke’s edema from healthy ones. Several classification methods have been used, such as decision trees, k-nearest neighbours, neural networks, support vector machines, Bayesian classification, regression analysis and linear discriminant. The experiments are based on two different databases. One of them is the commercial database Massachusetts Eye and Ear Infirmary (MEEI), recorded under highly controlled acoustical conditions, while the other one is an in-house database, recorded in a more realistic environment. The best results have been obtained by using the model based on neural networks, that achieved an overall accuracy of 100% on MEEI database and 95.49% on the in-house one. These are competitive results in comparison with those presented in the scientific literature and show the potential of these techniques to be employed within a support system for the diagnosis of Reinke’s edema.