wave in the cochlear fluid. This compressed wave causes a vertical vibration in the basilar membrane. The basilar membrane is mechanically tuned at different frequencies, and it plays a vital role in distributing sound energy by frequencies along the cochlea's length, as shown in Figure 2.  
The propagation of speech sound from a speaker to a listener involves the vibration of particles in the air. The vibrated air particles are perturbed near the lips. These perturbations (i.e., disturbances) of the air particles move like a chain reaction through the air to the listeners' peripheral auditory system. Voice pathology occurs when one or more human voice generation system components malfunction. The specific causes for this kind of malfunction are still research issues. However, researchers have discovered that calluses and swelling on vocal cords, vocal cord paralysis, vocal cord shutting, and spasmodic dysphonia are the leading causes of voice pathology. Other causes include hearing loss, neurological disorder, brain injury,  
Figure 1: A human voice generation system [2]. Figure 2: The tuning frequencies of the basilar membrane [4]. intellectual disability, and drug abuse. Researchers can find a comprehensive survey on the voice pathologies and their causes in [5; 6; 7]. Many voice pathologies have been reported in the literature. However, the American Speech-Language-Hearing Association (ASHA) has mentioned laryngitis as one of the three most common voice pathologies [8], which is investigated in this work. Laryngitis is caused by inflammation in the vocal folds [9]. This results in sounds being obstructed by the inflamed vocal folds as the air passes over them. Laryngitis can occur from voice overuse, smoking, and infection in the larynx [9]. The other reasons for laryngitis include excessive alcohol consumption and gastroesophageal reflux disease (GERD) [10]. Laryngitis makes the voice sound hoarse and weak. Both invasive and non-invasive methods are used for detecting voice pathologies. In invasive methods, physicians insert probes into the mouth using an endoscopic procedure. Laryngoscopy [11], stroboscopy [12], and laryngeal electromyography [13] are examples of such practices. In non-invasive methods, voice pathology is detected using voice signal processing [14; 15] techniques. These methods involve three significant steps, namely: (a) voice samples collection and analysis, (b) features' extraction, and (c) classification. Voice samples are collected in a sound environment. Then, the samples are analyzed, and voice features are extracted. The final step is to classify voice samples into control (i.e., healthy) and pathological. A classifier is commonly used for this purpose. A literature survey shows that several classifier algorithms have been popularly used for voice pathology detection. The resulting accuracies demonstrate that the classification accuracy mainly depends on the classifier algorithms and voice features [16; 17] used by the classifiers. Recently, deep learning algorithms have drawn considerable attention from researchers in this field. It has been shown in [18; 19; 20; 21; 22; 23; 24] that deep learning algorithms can play an essential role in voice pathology detection as they provide higher accuracies. The goal of this work is to focus on the possibility of using the existing technology of the cochlear simulation model noninvasively for the detection of pathological voice. The clinical tools used by the physicians rely on invasive technology that is unpleasant for the patients. Additionally, they sometimes rely on subjective assessment, especially for the voice pathology that lacks the structural abnormality. To overcome these limitations, we address a signal processing and deep learning-based technology that can help the clinicians for noninvasive objective assessment of voice disorder, and thus to provide relief for the patients from painful processes and to avoid the misdiagnosis that may result from subjective assessment.