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Predicting Speech-in-Noise Deficits from the Audiogram.

Daniel E Shub¹, Matthew J Makashay^1,2, Douglas S Brungart¹

¹National Military Audiology and Speech Center, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.

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|May 21, 2019

Summary

This summary is machine-generated.

A new binary threshold classifier effectively identifies individuals with poor speech-in-noise performance using audiograms, improving upon current military standards for hearing screenings. This enhances job safety and mission readiness for service members.

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Area of Science:

Audiology
Occupational Health
Military Medicine

Background:

Hearing is critical for many occupations, necessitating regular screenings.
Standard pure-tone audiograms may not fully assess job-related hearing ability, especially in noisy environments.
The U.S. military requires effective hearing assessments for service members' mission accomplishment.

Purpose of the Study:

To develop optimal strategies for identifying individuals with poor speech-in-noise performance using only audiogram data.
To evaluate various classification methods for predicting speech-in-noise deficits.

Main Methods:

Evaluated classifiers including generalized linear models (GLMs), Speech Intelligibility Index (SII), and binary threshold criteria on data from 5487 individuals.
Assessed classifier performance using sensitivity and specificity within a detection theoretic framework.
Analyzed the impact of classifiers on referral rates using data from 500,733 U.S. Army service members.

Main Results:

A binary threshold classifier, identified via iterative search, outperformed SII and GLM-based classifiers.
The optimal classifier demonstrated better identification of speech-in-noise deficits than current U.S. military standards.
Audiogram details showed a modest predictive value for speech-in-noise performance.

Conclusions:

The developed binary threshold classifier reliably improves detection of poor speech-in-noise performance compared to existing military criteria.
Significant gains in identifying impaired speech-in-noise understanding may require incorporating actual speech-in-noise tests.
Adopting the proposed classifier offers substantial benefits for military hearing conservation and readiness programs.