Hearing Aid Noise Reduction Algorithms Support Neural Speech Tracking by Enhancing Spectrotemporal Consistency of...

Previous works that have analyzed electroencephalographic (EEG) speech envelope encoding in hearing-aid users using stimulus reconstruction (SR) approaches have shown that noise reduction (NR) algorithms can improve the neural tracking of speech in noise. Beyond enhancing speech envelope encoding, NR may also restore the perceptibility of acoustic edges that trigger auditory late responses (ALRs), likely promoting more consistent ALR elicitation and thus enhancing brain-to-speech synchronization. To investigate this hypothesis, 26 hearing-impaired listeners were fitted with bilateral hearing aids and participated in an EEG experiment with the instruction to listen to a continuous single-speaker speech stimulus under speech-in-quiet conditions and speech-in-noise conditions with and without NR. Event-related potential analyses of neural responses to acoustic edges in the speech onset envelope revealed that P1 component amplitudes in averaged ALRs decreased significantly for speech-in-noise compared to speech-in-quiet conditions, but partially recovered when NR was activated. These observations correlated significantly with the degree of spectrotemporal consistency across single-trials for the associated ALR time-frequency component. Additionally, a SR analysis for the speech onset envelope produced qualitatively identical results that were highly correlated with those from the spectrotemporal consistency analysis. These findings suggest that hearing aid NR algorithms partly enhance neural speech tracking by restoring neural responses to acoustic edges. In turn, our results may support the future development of hearing aid processing strategies that could facilitate neural tracking of speech based on neural principles.