Voice Pathology Detection and Classification Using Convolutional Neural Network Model

• Published in: Applied sciences Vol. 10; no. 11; p. 3723
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.06.2020
• Subjects: Care and treatment; convolutional neural network; Diagnosis; Identification and classification; Machine learning; Methods; Neural networks; Pathology; residual network (ResNet34); Saarbrücken voice database; the vowel /a; Voice disorders; voice pathology classification; voice pathology detection; Iraq
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10113723

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.