Combined Signal Processing Based Techniques and Feed Forward Neural Networks for Pathological Voice Detection and Classification

This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks (FFNN). The important pathological voices such as Autism Spectrum Disorder (ASD) and Down Syndrome (DS) are considered for analysis. These pathological voices are known to manifest in different ways in the speech of children and adults. Therefore, it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects. The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques. In this work, three group of feature vectors such as perturbation measures, noise parameters and spectral-cepstral modeling are derived from the signals. The detection and classification is done by means of Feed Forward Neural Network (FFNN) classifier trained with Scaled Conjugate Gradient (SCG) algorithm. The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.