Evaluation of a semi-supervised self-adjustment fine-tuning procedure for hearing aids

Abstract

Objective

This study investigated the effects of different adjustment criteria and sound scenes on self-adjusted hearing-aid gain settings. Self-adjusted settings were evaluated for speech recognition in noise, perceived listening effort, and preference.

Design

This study evaluated a semi-supervised self-adjustment fine-tuning procedure that presents realistic everyday sound scenes in a laboratory environment, using a two-dimensional user interface, and enabling simultaneous changes in amplitude and spectral slope. While exploring the two-dimensional space of parameter settings, the hearing-aid users were instructed to optimise either listening comfort or speech understanding.

Study sample

Twenty experienced hearing aid users (median age 69.5 years) were invited to participate in this study.

Results

Adjustment criterion and sound scenes had a significant effect on preferred gain settings. No differences in signal-to-noise ratios required for 50% speech intelligibility or in the perceived listening effort were observed between the adjusted settings of the two adjustment criteria. There was a preference for the self-adjusted settings over the prescriptive first fit.

Conclusions

Listeners could reliably select their preferred gains to the two adjustment criteria and for different speech stimuli.

Keywords:

• Hearing aid fitting
• self-adjustment
• sound balance
• speech understanding
• listening comfort

Acknowledgments

The authors thank Iris Arweiler, Josef Chalupper and Martin Kinkel for very valuable suggestions for conducting the experiment, Sven Herrmann for conducting part of the participant tests, the Associate Editor and the reviewers for their helpful comments, and Jennifer Truempler for her English language support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded by the German Federal Ministry of Education and Research as part of “Medizintechnische Lösungen für die digitale Gesundheitsversorgung” (contract number: 13GW0167C) and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2177/1 – Project ID 390895286.