2024/06/11 by Wolf, Vera, Mueller, Michael
#Medicine and health
paper · doi:10.3205/zaud000042
In recent years, hearing aids have undergone enormous development: innovative signal processing and precise speech recognition have led to considerably improved speech understanding. At the same time, the fitting process has not changed significantly: a recommendation for suitable gain settings is mostly determined using prescriptive fitting formulae based on the wearers’ audiogram. However, this approach neglects differences in loudness perception, noise tolerance, and individual sound preferences. These issues can be addressed when end-users fine-tune their hearing aids via smartphone app-based digital assistants, which offer several advantages over fine-tuning by hearing care professionals. First, digital assistants allow highly individualized adaptations provided by artificial intelligence (AI). Second, the impact of memory bias is reduced as they can be directly used in the acoustically challenging situation. Finally, the applied setting updates can be evaluated directly, and hearing aid wearers may accept or reject the updates. In this short report, we discuss opportunities and challenges of such a digital assistant. We focus on the question of how hearing aid wearers prefer to use the digital assistant: directly in the problematic situation or afterwards. To this end, we analyze large-scale user data which shows that using the assistant in the problematic situation and afterwards are both popular. To meet these user expectations, we show how both modes of operation can be implemented in the digital assistant. Our findings highlight the need for validating app design in the field to maximize the usefulness of digital assistance systems.