SCH: Shallow and Deep Personalization for Hearing Aids

Approximately 44.1 million adults in the US suffered from hearing loss, according to recent statistics. Untreated hearing impairment affects communication and can contribute to social isolation, depression, dementia, and reduced quality of life. The primary intervention for sensorineural hearing loss and related psychosocial consequences is hearing aid (HA) amplification. Unfortunately, only 15–30% of those who could benefit from HAs use them. A prerequisite for the successful adoption of HAs is effective signal processing algorithms coupled with personalization methods to configure their many parameters to improve speech understanding, sound quality, and users' subjective preferences. Therefore, this proposal focuses on developing new signal processing algorithms and configuration methods that empower people with hearing loss to meet their individualized hearing needs. This project aims to develop two approaches for personalizing HAs with different trade-offs in the degree of personalization, the amount of user effort required to find a satisfactory configuration, and their effectiveness in handling hearing losses of varying severity. It will develop shallow personalization techniques for configuring the parameters of existing HA signal-processing pipelines. These approaches provide more personalization options than state-of-the-art over-the-counter HAs by using different sub-band processing gains, compression parameters, and noise-reduction settings depending on the auditory context. These techniques are most suitable for patients with mild-to-moderate sensorineural hearing loss. We will also develop deep personalization techniques for training and personalizing HAs that use deep neural networks to amplify sounds. A unique aspect of this approach is using electroencephalogram signals combined with user feedback to drive the personalization process. These algorithms will benefit patients with more severe hearing loss or those in challenging auditory environments. The intellectual merit of this proposal is the new advancements in machine learning that are necessary to enable patients to configure and effectively use their HAs. The proposed research is anticipated to empower patients to become more involved in hearing care, improve HA satisfaction, and enrich their social interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.