Effectiveness of deep neural networks in hearing aids for improving signal-to-noise ratio, speech recognition, and listener preference in background noise

Introduction Traditional approaches to improving speech perception in noise (SPIN) for hearing-aid users have centered on directional microphones and remote wireless technologies. Recent advances in artificial intelligence and machine learning offer new opportunities for enhancing the signal-to-noise ratio (SNR) through adaptive signal processing. In this study, we evaluated the efficacy of a novel deep neural network (DNN)-based algorithm, commercially implemented as Edge Mode™, in improving SPIN outcomes for individuals with sensorineural hearing loss beyond that of conventional environmental classification approaches. Methods The algorithm was evaluated using (1) objective KEMAR-based performance in seven real-world scenarios, (2) aided and unaided speech-in-noise performance in 20 individuals with SNHL, and (3) real-world subjective ratings via ecological momentary assessment (EMA) in 20 individuals with SNHL. Results Significant improvements in SPIN performance were observed on CNC+5, QuickSIN, and WIN, but not NST+5, likely due to the use of speech-shaped noise in the latter, suggesting the algorithm is optimized for multi-talker babble environments. SPIN gains were not predicted by unaided performance or degree of hearing loss, indicating individual variability in benefit, potentially due to differences in peripheral encoding or cognitive function. Furthermore, subjective EMA responses mirrored these improvements, supporting real-world utility. Discussion These findings demonstrate that DNN-based signal processing can meaningfully enhance speech understanding in complex listening environments, underscoring the potential of AI-powered features in modern hearing aids and highlighting the need for more personalized fitting strategies.