Belitz Chelzy, Ali Hussnain, Hansen John H L
Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, Texas, 75075 USA.
JASA Express Lett. 2021 Nov;1(11). doi: 10.1121/10.0007149. Epub 2021 Nov 19.
Although there exist nearly 35 × 10 hearing impaired people in the U.S., only an estimated 25% use hearing aids (HA), while others elect not to use prescribed HAs. Lack of HA acceptance can be attributed to several factors including (i) performance variability in diverse environments, (ii) time-to-convergence for best HA operating configuration, (iii) unrealistic expectations, and (iv) cost/insurance. This study examines a nationwide dataset of pure-tone audiograms and HA fitting configurations. An overview of data characteristics is presented, followed by use of machine learning clustering to suggest ways of obtaining effective starting configurations, thereby reducing time-to-convergence to improve HA retention.
尽管美国有近3500万听力受损者，但据估计只有25%的人使用助听器，而其他人则选择不使用医生开具的助听器。人们不接受助听器可归因于几个因素，包括：（i）在不同环境中的性能差异；（ii）达到最佳助听器操作配置的收敛时间；（iii）不切实际的期望；以及（iv）成本/保险。本研究考察了一个全国范围的纯音听力图和助听器适配配置数据集。文章首先介绍了数据特征，然后使用机器学习聚类方法，提出获得有效初始配置的方法，从而缩短收敛时间，提高助听器的留存率。
