Measuring and modeling context-dependent preferences for hearing aid settings

Alessandro Pasta*, Michael Kai Petersen, Kasper Juul Jensen, Niels Henrik Pontoppidan, Jakob Eg Larsen, Jeppe Høy Christensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Despite having individual perceptual preferences toward sounds, hearing aid users often end up with default hearing aid settings that have no contextual awareness. However, the introduction of smartphone-connected hearing aids has enabled a rethinking of hearing aids as user-adaptive systems considering both individual and contextual differences. In this study, we aimed to investigate the feasibility of such context-aware system for providing hearing aid users with a number of relevant hearing aid settings to choose from. During normal real-world hearing aid usage, we applied a smartphone-based method for capturing participants’ listening experience and audiological preference for different intervention levels of three audiological parameters (Noise Reduction, Brightness, Soft Gain). Concurrently, we collected contextual data as both self-reports (listening environment and listening intention) and continuous data logging of the acoustic environment (sound pressure level, signal-to-noise ratio). First, we found that having access to different intervention levels of the Brightness and Soft Gain parameters affected listening satisfaction. Second, for all three audiological parameters, the perceived usefulness of having access to different intervention levels was significantly modulated by context. Third, contextual data improved the prediction of both explicit and implicit intervention level preferences. Our findings highlight that context has a significant impact on hearing aid preferences across participants and that contextual data logging can help reduce the space of potential interventions in a user-adaptive system so that the most useful and preferred settings can be offered. Moreover, the proposed mixed-effects model is suitable for capturing predictions on an individual level and could also be expanded to predictions on a group level by including relevant user features.

Original languageEnglish
JournalUser Modeling and User-Adapted Interaction
Pages (from-to)977–998
Publication statusPublished - 2022


  • Contextual awareness
  • Hearing aids
  • Hearing healthcare
  • User preferences
  • User satisfaction
  • User-adaptive system


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