Understanding speech in noise (SiN) is a complex task that recruits multiple cortical subsystems. There is a variance in individuals' ability to understand SiN that cannot be explained by simple hearing profiles, which suggests that central factors may underlie the variance in SiN ability. Here, we elucidated a few cortical functions involved during a SiN task and their contributions to individual variance using both within- and across-subject approaches. Through our within-subject analysis of source-localized electroencephalography, we investigated how acoustic signal-to-noise ratio (SNR) alters cortical evoked responses to a target word across the speech recognition areas, finding stronger responses in left supramarginal gyrus (SMG, BA40 the dorsal lexicon area) with quieter noise. Through an individual differences approach, we found that listeners show different neural sensitivity to the background noise and target speech, reflected in the amplitude ratio of earlier auditory-cortical responses to speech and noise, named as an internal SNR. Listeners with better internal SNR showed better SiN performance. Further, we found that the post-speech time SMG activity explains a further amount of variance in SiN performance that is not accounted for by internal SNR. This result demonstrates that at least two cortical processes contribute to SiN performance independently: pre-target time processing to attenuate neural representation of background noise and post-target time processing to extract information from speech sounds.

Introduction: Older listeners have difficulty understanding speech in unfavorable listening conditions. To compensate for acoustic degradation, cognitive processing skills, such as working memory, need to be engaged. Despite prior findings on the association between working memory and speech recognition in various listening conditions, it is not yet clear whether the modality of stimuli presentation for working memory tasks should be auditory or visual. Given the modality-specific characteristics of working memory, we hypothesized that auditory working memory capacity could predict speech recognition performance in adverse listening conditions for older listeners and that the contribution of auditory working memory to speech recognition would depend on the task and listening condition. Methods: Seventy-six older listeners and twenty younger listeners completed four kinds of auditory working memory tasks, including digit and speech span tasks, and sentence recognition tasks in four different listening conditions having multi-talker noise and time-compression. For older listeners, cognitive function was screened using the Mini-Mental Status Examination, and audibility was assured. Results: Auditory working memory, as measured by listening span, significantly predicted speech recognition performance in adverse listening conditions for older listeners. A linear regression model showed speech recognition performance for older listeners could be explained by auditory working memory whilst controlling for the impact of age and hearing sensitivity. Discussion: Measuring working memory in the auditory modality facilitated explaining the variance in speech recognition in adverse listening conditions for older listeners. The linguistic features and the complexity of the auditory stimuli may affect the association between working memory and speech recognition performance. Conclusion: We demonstrated the contribution of auditory working memory to speech recognition in unfavorable listening conditions in older populations. Taking the modality-specific characteristics of working memory into account may be a key to better understand the difficulty in speech recognition in daily listening conditions for older listeners.

Selective attention can be a useful tactic for speech-in-noise (SiN) interpretation as it strengthens cortical responses to attended sensory inputs while suppressing others. This cortical process is referred to as attentional modulation. Our earlier study showed that a neurofeedback training paradigm was effective for improving the attentional modulation of cortical auditory evoked responses. However, it was unclear how such neurofeedback training improved attentional modulation. This paper attempts to unveil what neural mechanisms underlie strengthened auditory selective attention during the neurofeedback training paradigm. Our EEG time–frequency analysis found that, when spatial auditory attention was focused, a fronto-parietal brain network was activated. Additionally, the neurofeedback training increased beta oscillation, which may imply top-down processing was used to anticipate the sound to be attended selectively with prior information. When the subjects were attending to the sound from the right, they exhibited more alpha oscillation in the right parietal cortex during the final session compared to the first, indicating improved spatial inhibitory processing to suppress sounds from the left. After the four-week training period, the temporal cortex exhibited improved attentional modulation of beta oscillation. This suggests strengthened neural activity to predict the target. Moreover, there was an improvement in the strength of attentional modulation on cortical evoked responses to sounds. The Placebo Group, who experienced similar attention training with the exception that feedback was based simply on behavioral accuracy, did not experience these training effects. These findings demonstrate how neurofeedback training effectively improves the neural mechanisms underlying auditory selective attention.

Noise reduction (NR) algorithms are employed in nearly all commercially available hearing aids to attenuate background noise. However, NR processing also involves undesirable speech distortions, leading to variability in hearing outcomes among individuals with different noise tolerance. Leveraging 30 participants with normal hearing engaged in speech-in-noise tasks, the present study examined whether the cortical measure of neural signal-to-noise ratio (SNR)—the amplitude ratio of auditory evoked responses to target speech onset and noise onset—could predict individual variability in NR outcomes with varying strength, thus serving as a reliable indicator of individual noise tolerance. In addition, we also measured subjective ratings of noise tolerance to see if these measures could capture different perspectives on individual noise tolerance. Results indicated a significant correlation between neural SNR and NR outcomes that intensified with increasing strength of NR processing. While subjective ratings of noise tolerance were not correlated with the neural SNR, noise-tolerance ratings could predict outcomes with stronger NR processing and account for additional variance in the regression model, although the effect was limited. Our findings underscore the importance of accurately assessing an individual’s noise tolerance characteristics in predicting perceptual benefits from various NR processing methods and suggest the advantage of incorporating both cortical and subjective measures in the relevant methodologies.

Background/Objectives: Individuals with similar hearing sensitivity exhibit varying levels of tolerance to background noise, a trait tied to unique individual characteristics that affect their responsiveness to noise reduction (NR) processing in hearing aids. The present study aimed to capture such individual characteristics by employing electrophysiological measures and subjective noise-tolerance profiles, and both were analyzed in relation to speech-in-noise performance and NR outcomes. Methods: From a sample of 42 participants with normal hearing, the neural signal-to-noise ratio (SNR)—a cortical index comparing the amplitude ratio between auditory evoked responses to target speech onset versus noise onset—was calculated, and individual noise-tolerance profiles were also derived using k-means cluster analysis to classify participants into distinct subgroups. Results: The neural SNR showed significant correlations with speech-in-noise performance and NR outcomes with varying strength. In contrast, noise-tolerance subgroups did not show meaningful group-level differences in either speech-in-noise or NR outcomes. The neural SNR and noise-tolerance profiles were found to be statistically independent. Conclusions: While the neural SNR reliably predicted perceptual performance in background noise and NR outcomes, our noise-tolerance profiles lacked sufficient sensitivity. Still, subjective ratings of individual noise tolerance are clinically accessible, and thus, integrating both physiology and subjective measures in the same cohort is a valuable strategy.