Software corrections of vocal disorders.

We discuss how vocal disorders can be post-corrected via a simple nonlinear noise reduction scheme. This work is motivated by the need of a better understanding of voice dysfunctions. This would entail a 2-fold advantage for affected patients: Physicians can perform better surgical interventions and on the other hand researchers can try to build up devices that can help to improve voice quality, i.e. in a phone conversation, avoiding any surgical treatment. As a first step, a proper signal classification is performed, through the idea of geometric signal separation in a feature space. Then through the analysis of the different regions populated by the samples coming from healthy people and from patients affected by T1A glottis cancer, one is able to understand which kind of interventions are necessary in order to correct the illness, i.e. to move the corresponding feature vector from the sick region to the healthy one. We discuss such a filter and show its performance.