Voice Pathology Detection and Classification Using Convolutional Neural Network Model

Mazin Abed Mohammed,Fahad Taha Al-Dhief,Salama A Mostafa,Ibon Oleagordia,Mohd Khanapi Abd Ghani,Begonya Garcia-Zapirain,Karrar Hameed Abdulkareem,Hosam Alhakami,Mashael S Maashi

doi:10.3390/app10113723

Abstract

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.

Highlights

There are many factors that can caused by voice pathologies
We propose automatic rapid voice pathology detection based on a deep learning classifier, namely a DNN system for voice pathology detection
We propose a deep learning Convolutional Neural Network (CNN) model to perform pathology detection based on numerical analysis of voice signals

Summary

Introduction

There are many factors that can caused by voice pathologies. The voice pathology has a negative impact on vibration regularity and voice functionality, which leads to an increase in vocal noise. The normal voice turned to be tense, weak and hoarse [2] that affects the quality. Sci. 2020, 10, 3723 of voice [3]. The current vocal pathology detection methods have a biased evaluation based on subjective matters [4]. An example of the subjective evaluation is auditory-perceptual assessment in hospitals, which is widely applied by visual laryngostroboscopy assessment [5]. Several clinical examinations are applied for auditory-perceptual parameters to scale the rate of severity diagnosis [6]

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Conclusion