Non supervised neural net applied to the detection of voice impairment

Most vocal and voice diseases cause changes in the voice. ENT clinicians use acoustic voice analysis to characterise pathological voices. The authors have focused their task in detection of impaired voices by means of neural network technology (ANN) and acoustic analysis. Former and actual works demonstrates that impaired voice detection can be carried out by means of supervised neural nets: MLP (multilayer perceptron). This paper is focussed in the task of detection of pathological voices by means of non-supervised neural nets (Kohonen self organising maps), comparing results with those obtained using supervised neural nets (MLPs). The aim of this paper is to study and compare two neural net based methods to be used for the detection of impaired voices: supervised (MLP ANNs) and non-supervised neural nets (Kohonen ANNs). Voice registers are parameterised by means of acoustic parameters