Estimating hearing aid fitting presets with machine learning-based clustering strategies.

Journal: JASA express letters
Published Date: Nov 19, 2021

Abstract

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.

Authors

  • Chelzy Belitz
    Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, Texas, 75075 USA.
  • Hussnain Ali
    Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, Texas, 75075 USA.
  • John H L Hansen
    CRSS: Center for Robust Speech Systems; Cochlear Implant Processing Laboratory (CILab), Department of Electrical and Computer Engineering, University of Texas at Dallas, USA.

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