Personalization of multi-parameter hearing aids involves an initial fitting followed by a manual knowledge-based trial-and-error fine-tuning from ambiguous verbal user feedback. The result is an often suboptimal HA setting whereby the full potential of modern hearing aids is not utilized. This article proposes an interactive hearing-aid personalization system that obtains an optimal individual setting of the hearing aids from direct perceptual user feedback. Results obtained with ten hearing-impaired subjects show that ten to twenty pairwise user assessments between different settings-equivalent to 5-10 min-is sufficient for personalization of up to four hearing-aid parameters. A setting obtained by the system was significantly preferred by the subject over the initial fitting, and the obtained setting could be reproduced with reasonable precision. The system may have potential for clinical usage to assist both the hearing-care professional and the user.

Due to the large amount of options offered by the vast number of adjustable parameters in modern digital hearing aids, it is becoming increasingly daunting-even for a fine-tuning professional-to perform parameter fine tuning to satisfactorily meet the preference of the hearing aid user. In addition, the communication between the fine-tuning professional and the hearing aid user might muddle the task. In the present paper, an interactive system is proposed to ease and speed up fine tuning of hearing aids to suit the preference of the individual user. The system simultaneously makes the user conscious of his own preferences while the system itself learns the user's preference. Since the learning is based on probabilistic modeling concepts, the system handles inconsistent user feedback efficiently. Experiments with hearing impaired subjects show that the system quickly discovers individual preferred hearing-aid settings which are consistent across consecutive fine-tuning sessions for each user.