Neural tracking of continuous speech in adverse acoustic conditions among healthy adults with normal hearing and hearing loss: A systematic review
Bibliographic record
Abstract
The study of neural speech tracking (NST) has gained increasing attention in the field of auditory neuroscience in recent years. However, its contribution to speech perception in noise (SPiN), especially regarding aging and hearing loss, has yet to be fully explored. This systematic review examined NST in adults with and without hearing loss, focusing on its modulation by age, hearing impairment, and adverse acoustic conditions, as well as its relationship with behavioral SPiN performance. A systematic literature search identified studies using electroencephalography (EEG) or magnetoencephalography (MEG) to investigate NST in continuous speech processing under adverse acoustic conditions. Studies included participants with and without hearing loss, excluding those with neurological disorders. Various NST methods, including forward and backward modeling, coherence, and cross-correlation, were examined. Fifty-four studies met the inclusion criteria. Most studies focused on young adults, with fewer studies including older adults or individuals with hearing loss. Findings suggest that older adults exhibit increased NST compared to younger adults, potentially reflecting compensatory mechanisms for auditory processing declines. Similarly, hearing impairment was generally associated with enhanced NST, likely due to altered neural encoding and increased reliance on cognitive resources. The impact of adverse acoustic conditions, as reflected by the signal-to-noise ratio (SNR), on NST was predominantly negative, with NST decreasing as noise levels increased. However, some studies suggested a non-linear relationship, with NST peaking at intermediate SNRs. Furthermore, most studies reported a positive correlation between NST and SPiN performance, typically observed across individuals or conditions within homogeneous groups or pooled samples. While stronger tracking was generally associated with better behavioral outcomes, this relationship does not imply that higher NST always corresponds to better performance across different populations. Age and hearing loss appear to modulate NST, likely through both neural compensation and auditory processing adaptations. The complex effects of SNR on NST highlight the need for additional research to better understand its underlying mechanisms. Future studies should delve deeper into the interplay between aging, cognition, and auditory deficits in shaping NST, offering a more comprehensive understanding of speech processing in challenging acoustic environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".