Voice Pathology Detection and Classification Using Convolutional Neural Network Model
M, Mohammed, . 2020
Voice pathology disorders can be effectively detected using computer‐aided voice
pathology classification tools. These tools can diagnose voice pathologies at an early stage and
offering appropriate treatment. This study aims to develop a powerful feature extraction voice
pathology detection tool based on Deep Learning. In this paper, a pre‐trained Convolutional Neural
Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy.
This study also proposes a distinguished training method combined with various training strategies
in order to generalize the application of the proposed system on a wide range of problems related
to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken
voice database (SVD). The experimental results show the proposed CNN method for speech
pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1‐Score
and Recall. The proposed system shows a high capability of the real‐clinical application that offering
a fast‐automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.
