# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
###### 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 Saarbrucken 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. v 20202026 May 202027 May 20202030 April 202026 May 20  
of voice [3]. To date, 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]. However, those evaluation methods are subject to parameter sensitivity and are also time consuming and laborious [7]. In addition, these methods require a physical patient examination in the clinic which could be difficult for patients with severe conditions. An example of objective evaluation is using a computer-aided tool to identify and analyse vocal signals without any surgical intervention. The automatic detection can also recognize inaudible sounds [1]. These evaluation methods are not subjective as they do not depend on a human decision. Besides, they are easy to apply since the voice recordings can be made available remotely via different internet recoding applications. Therefore, some studies such as [8] have developed vocal processing methods to determine the vocal pathology aspects can be effectively combined with a machine learning method to detect the voice pathology automatically in one framework to accurately distinguish healthy people from people with voice pathologies. In the literature, various voice pathology databases have been widely applied for the objective evaluation of voice pathology. The most common voice pathology databases are the Saarbruecken Voice Database (SVD) [9], Arabic Voice Pathology Database (AVPD) [10] and the Massachusetts Eye and Ear Infirmary Database (MEEI) [11]. The vocalization of the vowel /a/. [1, 11] is available in many language databases [2], therefore, it is commonly analysed by researchers. Other combinations of vowels are also analyzed by researchers [1, 12]. Notably, the majority of the researchers in the voice pathologies community have limited the datasets to specific pathologies sets [12]. Usually, the clinical interpretation of vocal features is conducted before the process of pathology detection [13]. Examples of vocal features are glottal-to-noise excitation ratio (GNE) [14], Mel frequency cepstral coefficients (MFCC) [15], multidimensional voice program parameters (MDVP) [16] and many others. See [17] for more details about the settings of speech pathologies. Once the vocal features are extracted, many conventional classification methods are applied for voice pathology detection. For detection purposes, most studies have used Random Forests (RF), Artificial Neural Networks (ANN) [18], Gaussian Mixture Models (GMM), Support Vector Machines (SVM) and other classifiers [19, 20]. It is observed that the study results show notable differences.