## 4 Discussion  
The primary goal of this research is to create and validate a computerized Arabic word-in-noise speech perception screening tool that can be used outside of the clinical setting. Outside-clinic tests are essential as they can be effective in rural areas and under political, security, or health pandemic constraints where proper healthcare services are hard to reach [33; 37; 38]. Developing and validating a speech perception screening tool depends on several factors, such as test duration, linguistics, and the type and intensity of noise. This study validates a proposed Arabic word-in-noise assessment test, reporting the advantages of a  
Figure 4: The across-subjects, across-list mean (\(\pm\)SEM) percent-correct word identification score for white noise and multi-talker babble background noise. (\({}^{**}\)_p_ < 0.01). Figure 3: A cross-list mean (\(\pm\)SEM) percent-correct word identification score for each participant for both white noise and multi-talker babble background noise. short testing time and affordable hardware. The proposed test also encompasses several SNR levels that vary in difficulty to enhance the accuracy of assessing speech intelligibility. Additionally, there are two main categories of background noise, known as masking noises: stationary or fluctuant [39]. Noise with a changing intensity over time is fluctuant, whereas stationary noise has a fixed intensity over time. Furthermore, speech-in-noise audiometry tests often incorporate white noise or speech spectrum noise as a masking noise [39; 40]. Although it does not reflect daily life situations, stationary white noise is most commonly used in speech-in-test clinical testing for its simplicity and ease of calibration [39]. In contrast, spectrum noise, such as multi-talker babble noise, is more representative of daily life situations. Thus, the two types of background noises were tested in this pilot study to investigate their impact on speech perception in healthy individuals. The results indicate that the performance of people with normal hearing was significantly higher in the presence of white noise (80.14%) compared to multi-talker babble noise (74.17%). This implies that listeners with normal hearing are affected differently by the type of background noise, which is in line with other studies showing that word recognition scores are lower in the presence of multi-talker babble noise [41; 42; 43]. The results of the present investigation also suggest that the use of white background noise is less challenging and more consistent compared to multi-talker babble noise, at least for Arabic-speaking adults with normal hearing. The use of white background noise could also be more favorable over multi-talker babble noise as the test is developed to be used out of the sound booth and clinical setup where environmental noise is less controlled. However, further investigations are needed to confirm the effect of background noise in diverse practical settings. In comparison with the few existing Arabic speech-in-noise and word-in-noise tests (Arabic matrix test, Speech Perception of Words in Noise test, and the PAAST), the versatile Arabic word-in-noise test proposed in this study has some design advantages. First, the proposed test is a word-based screening tool that is much easier to complete and comprehend than the sentence-based tests used in the Arabic matrix test. Additionally, this versatile test has its testing words embedded in varying SNR levels, which enable primary care personnel to have a better screening result compared to the paradigm of a zero SNR variation between the presented word and the background noise as in the Speech Perception of Words in Noise test. Finally, the PAAST screening test has a limited targeted population where the word lists in the study can be recognized by school-age children, youth, and elderly populations. The current study had some limitations, including the lack of enrollment of an age-matched group with hearing loss.