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 ORIGINAL ARTICLE
Year : 2023  |  Volume : 25  |  Issue : 117  |  Page : 104--112

Development of an Arabic “Command in Noise” Hearing Test to Assess Fitness for Duty


1 Institute of Sound and Vibration Research, University of Southampton, Southampton; Department of Otorhinolaryngology–Head and Neck Surgery, King Abdul-Aziz University, Jeddah, Saudi Arabia, UK
2 Institute of Sound and Vibration Research, University of Southampton, Southampton, UK
3 Department of Otorhinolaryngology–Head and Neck Surgery, King Abdul-Aziz University, Jeddah, Saudi Arabia

Correspondence Address:
Iman Rawas
Institute of Sound and Vibration Research, University of Southampton, Southampton
UK
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/nah.nah_69_22

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Objective: The goal is to implement the developed speech material in a hearing test to assess auditory fitness for duty (AFFD), specifically in areas where the intelligibility of spoken commands is essential. Design: In study 1, a speech corpus with equal intelligibility was constructed using constant stimuli to test each target word’s psychometric functions. Study 2 used an adaptive interleaving procedure to maximize equalized terms. Study 3 used Monte Carlo simulations to determine speech test accuracy. Study sample: Study 1 (n = 24) and study 2 (n = 20) were completed by civilians with normal hearing. Study 3 ran 10,000 simulations per condition across various conditions varying in slopes and speech recognition thresholds (SRTs). Results: Studies 1 and 2 produced three 8-word wordlists. The mean, standard deviation in dB SNR is −13.1 1.2 for wordlist 1, −13.7 1.6 for wordlist 2, and −13.7 1.3 for wordlist 3, with word SRTs within 3.4 dB SNR. Study 3 revealed that a 6 dB SNR range is appropriate for equally understandable speech using a closed-set adaptive technique. Conclusion: The developed speech corpus may be used in an AFFD measure. Concerning the homogeneity of the speech in noise test material, care should be taken when generalizing and using ranges and standard deviations from multiple tests.






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