###### Abstract
The babble level increases in 5 dB increments while the sentences are presented at a fixed level. Another standard test is the Bamford Kowal-Bench speech-in-noise test (BKB-SIN). It contains 18 lists of sentences in the presence of a four-talker babbling noise in the background [19]. The Hearing in Noise Test for Children (HINT-C) is also used to diagnose speech perception conditions in children [20; 21]. Finally, one of the most widely used tests in audiology clinics is the word-in-noise (WIN) test. The WIN test has the advantage of being relatively easier to administer and requires less working memory and linguistic context during the performance compared to the speech-in-noise test [22]. One of the few Arabic versions of the speech-in-noise tests is the Arabic matrix test. The Arabic matrix test generates sentences randomly from a matrix that are presented with and without background noise [23]. The Speech Perception of Words in Noise test is an Arabic WIN test in which words are presented at zero SNR in a cafeteria background noise [24]. Another example of an Arabic WIN test is the Pediatric Arabic Auditory Speech Test (PAAST), which was developed to investigate speech perception in children [25]. The main difference between the PAAST and the aforementioned Speech Perception of Words in Noise test is how the responses are collected. In the PAAST, children are asked to choose an image that corresponds to a presented word they heard [25]. Although speech-in-noise tests can provide a good assessment of speech perception inside hospitals and clinics, it is difficult to access specialized healthcare facilities for some individuals, communities, and countries, especially during pandemic and quarantine situations or even in rural areas. For that, several investigators have designed, developed, and validated computer-based applications for speech perception assessment outside the clinic to help with the early detection of hearing impairments. Outside clinic tests have the potential to alert individuals of possible hearing problems and a referral to secondary-level healthcare could confirm the level and type of the hearing impairment. For instance, computer-based or smartphone applications for audiological screening have shown reasonable efficiency [25; 26; 27; 28; 29; 30; 31]. For example, [PERSON] et al. [28] investigated the feasibility of implementing an existing mobile health application in a community-based setup to screen the hearing of young children. The study examined a mobile application, hearScreenTM, which can be easily trained to be used by non-professional personnel such as school teachers, social workers, or nurses. The employment of the hearScreenTM solution led to a referral rate of 24.9% in preschool children in a poor community. This study concluded that smartphone-based hearing screening could be a viable tool to detect unidentified hearing impairments with minimal training of non-professional individuals. In addition, [PERSON] et al. [32] developed and validated an application named Ouviu that can be used pre-clinically to identify undetected hearing problems outside the clinical sound booth. The application conducts a hearing test that screens for 500, 1000, 2000, 5000, and 8000 Hz at low, medium, and high intensities. Results showed that Ouviu is capable of measuring environmental noise and outperformed the HearCheck screener equipment. [PERSON] et al. [33] assessed auditory temporal resolution by utilizing an auditory gap-in-noise test. The authors compared the assessment of auditory temporal resolution in a controlled environment (inside a soundproof booth) versus assessing auditory temporal resolution in a normal room with an acceptable surrounding noise level (below 50 dB). The results showed that gap detection thresholds obtained outside the soundproof booth are reliable and comparable to the standard method currently used in a controlled clinical setting (inside a soundproof booth).