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.
Proceedings. Ieee International Conference on Acoustics, Speech and Signal Processing (icassp), 2013, p. 398-402
Hearing aid personalization; Bayesian learning; Gaussian processes; Active learning; Preference learning
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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)International Conference on Acoustics, Speech and Signal Processing