Deep Learning Approach for Voice Pathology Detection and Classification

Abstract

A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current paper. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms. The proposed architecture has five different stages. The results are recorded in terms of nine different classification score indices, which are mean average precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews correlation coefficient, and the Cohen’s kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.