Voice Pathology Detection and Classification Using Convolutional Neural Network Model - Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Mostafa, Salama A.; Khanapi Abd Ghani, Mohd; Maashi, Mashael S.; Garcia-Zapirain, Begonya; Oleagordia, Ibon; Alhakami, Hosam; AL-Dhief, Fahad Taha (Applied sciences, 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.