This paper proposes the use of a similarity measure based on information theory called correntropy for the automatic classification of pathological voices. By using correntropy, it is possible to obtain descriptors that aggregate distinct spectral characteristics for healthy and pathological voices. Experiments using computational simulation demonstrate that such descriptors are very efficient in the characterization of vocal dysfunctions, leading to a success rate of 97% in the classification. With this new architecture, the classification process of vocal pathologies becomes much more simple and efficient.
In REF , the authors used an information theory-based technique, called correntropy, to distinguish between normal and pathological voices.
Classification System of Pathological Voices Using Correntropy
Mathematical Problems in Engineering
Mathematics