National Military Audiology and Speech Center, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.
Army Hearing Division, Army Public Health Center, Aberdeen Proving Ground, Maryland, USA.
Ear Hear. 2020 Jan/Feb;41(1):39-54. doi: 10.1097/AUD.0000000000000745.
In occupations that involve hearing critical tasks, individuals need to undergo periodic hearing screenings to ensure that they have not developed hearing losses that could impair their ability to safely and effectively perform their jobs. Most periodic hearing screenings are limited to pure-tone audiograms, but in many cases, the ability to understand speech in noisy environments may be more important to functional job performance than the ability to detect quiet sounds. The ability to use audiometric threshold data to identify individuals with poor speech-in-noise performance is of particular interest to the U.S. military, which has an ongoing responsibility to ensure that its service members (SMs) have the hearing abilities they require to accomplish their mission. This work investigates the development of optimal strategies for identifying individuals with poor speech-in-noise performance from the audiogram.
Data from 5487 individuals were used to evaluate a range of classifiers, based exclusively on the pure-tone audiogram, for identifying individuals who have deficits in understanding speech in noise. The classifiers evaluated were based on generalized linear models (GLMs), the speech intelligibility index (SII), binary threshold criteria, and current standards used by the U.S. military. The classifiers were evaluated in a detection theoretic framework where the sensitivity and specificity of the classifiers were quantified. In addition to the performance of these classifiers for identifying individuals with deficits understanding speech in noise, data from 500,733 U.S. Army SMs were used to understand how the classifiers would affect the number of SMs being referred for additional testing.
A classifier based on binary threshold criteria that was identified through an iterative search procedure outperformed a classifier based on the SII and ones based on GLMs with large numbers of fitted parameters. This suggests that the saturating nature of the SII is important, but that the weights of frequency channels are not optimal for identifying individuals with deficits understanding speech in noise. It is possible that a highly complicated model with many free parameters could outperform the classifiers considered here, but there was only a modest difference between the performance of a classifier based on a GLM with 26 fitted parameters and one based on a simple all-frequency pure-tone average. This suggests that the details of the audiogram are a relatively insensitive predictor of performance in speech-in-noise tasks.
The best classifier identified in this study, which was a binary threshold classifier derived from an iterative search process, does appear to reliably outperform the current thresholds criteria used by the U.S. military to identify individuals with abnormally poor speech-in-noise performance, both in terms of fewer false alarms and a greater hit rate. Substantial improvements in the ability to detect SMs with impaired speech-in-noise performance can likely only be obtained by adding some form of speech-in-noise testing to the hearing monitoring program. While the improvements were modest, the overall benefit of adopting the proposed classifier is likely substantial given the number of SMs enrolled in U.S. military hearing conservation and readiness programs.
在涉及听力关键任务的职业中，个人需要定期进行听力筛查，以确保他们没有出现听力损失，从而影响其安全有效地执行工作的能力。大多数定期听力筛查仅限于纯音听力图，但在许多情况下，在嘈杂环境中理解言语的能力可能比检测安静声音的能力对功能工作绩效更为重要。利用听阈数据识别言语噪声环境下表现不佳的个体的能力，是美国军方特别感兴趣的问题，因为军方一直有责任确保其军人（SMs）具备完成任务所需的听力能力。这项工作旨在研究从听力图中识别言语噪声环境下表现不佳个体的最佳策略的制定。
使用来自 5487 名个体的数据，基于纯音听力图，评估了一系列分类器，用于识别理解噪声中言语能力有缺陷的个体。评估的分类器基于广义线性模型（GLMs）、言语可懂度指数（SII）、二进制阈值标准以及美国军方当前使用的标准。这些分类器是在检测理论框架中进行评估的，其中量化了分类器的灵敏度和特异性。除了这些分类器识别理解噪声中言语能力有缺陷的个体的性能外，还使用了来自 500733 名美国陆军军人的数据，以了解分类器将如何影响需要进行额外测试的军人数量。
通过迭代搜索过程确定的基于二进制阈值标准的分类器优于基于 SII 的分类器和基于具有大量拟合参数的 GLMs 的分类器。这表明 SII 的饱和性质很重要，但频率通道的权重对于识别理解噪声中言语能力有缺陷的个体并不是最佳的。一个具有许多自由参数的高度复杂模型可能会优于这里考虑的分类器，但基于具有 26 个拟合参数的 GLM 的分类器与基于全频纯音平均的分类器之间的性能差异仅为适度。这表明听力图的细节是言语噪声任务表现的相对不敏感预测因子。
在这项研究中确定的最佳分类器是一种从迭代搜索过程中得出的二进制阈值分类器，它似乎确实能够可靠地优于美国军方当前用于识别言语噪声环境下表现异常差的个体的阈值标准，无论是在误报率较低还是击中率较高方面都是如此。要提高检测言语噪声环境下表现受损军人的能力，可能只能通过向听力监测计划中添加某种形式的言语噪声测试来实现。虽然改进幅度较小，但鉴于美国军方听力保护和准备计划中招募的军人数量，采用建议的分类器的总体收益可能很大。
