Belitz Chelzy, Ali Hussnain, Hansen John H L
CRSS: Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, TX, USA.
Interspeech. 2019 Sep;2019:2325-2329. doi: 10.21437/interspeech.2019-3091.
Of the nearly 35 million people in the USA who are hearing impaired, only an estimated 25% use hearing aids (HA). A good number of HAs are prescribed but not used partially because of the time to convergence for best operation between the audiologist and user. To improve HA retention, it is suggested that a machine learning (ML) protocol could be established which improves initial HA configurations given a user's pure-tone audiogram. This study examines a ML clustering method to predict the best initial HA fitting from a corpus of over 90,000 audiogram-fitting pairs collected from hearing centers throughout the USA. We first examine the final HA comfort targets to determine a limited number of preset configurations using several multi-dimensional clustering methods (Birch, Ward, and k-means). The goal is to reduce the amount of adjustments between the centroid, selected as a fitting configuration to represent the cluster, and the final HA configurations. This may be used to reduce the adjustment cycles for HAs or as preset starting configurations for personal sound amplification products (PSAPs). Using various classification methods, audiograms are mapped to a limited number of potential preset configurations. Finally, the average adjustment between the preset fitting targets and the final fitting targets is examined.
在美国近3500万听力受损者中，估计只有25%的人使用助听器（HA）。相当数量的助听器已被开出处方，但却未被使用，部分原因是听力学家和使用者之间达到最佳操作的磨合时间问题。为了提高助听器的留存率，有人建议可以建立一种机器学习（ML）协议，根据用户的纯音听力图来改善初始助听器配置。本研究考察了一种机器学习聚类方法，以从美国各地听力中心收集的9万多对听力图-适配对的语料库中预测最佳的初始助听器适配情况。我们首先检查最终的助听器舒适度目标，使用几种多维聚类方法（Birch、Ward和k均值）来确定有限数量的预设配置。目标是减少作为代表聚类的适配配置所选的质心与最终助听器配置之间的调整量。这可用于减少助听器的调整周期，或作为个人声音放大产品（PSAP）的预设起始配置。使用各种分类方法，将听力图映射到有限数量的潜在预设配置。最后，检查预设适配目标与最终适配目标之间的平均调整情况。
