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

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 const...

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Detalles Bibliográficos
Autores principales: Rawas, Iman, Rowan, Daniel, Semeraro, Hannah, Bleeck, Stefan, Bamanie, Afaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301907/
https://www.ncbi.nlm.nih.gov/pubmed/37203127
http://dx.doi.org/10.4103/nah.nah_69_22
Descripción
Sumario: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.