Enhancing speech perception in noise through articulation
Bibliographic record
Abstract
Considerable debate exists about the interplay between auditory and motor speech systems. Some argue for common neural mechanisms, whereas others assert that there are few shared resources. In four experiments, we tested the hypothesis that priming the speech motor system by repeating syllable pairs aloud improves subsequent syllable discrimination in noise compared with a priming discrimination task involving same-different judgments via button presses. Our results consistently showed that participants who engaged in syllable repetition performed better in syllable discrimination in noise than those who engaged in the priming discrimination task. This gain in accuracy was observed for primed and new syllable pairs, highlighting increased sensitivity to phonological details. The benefits were comparable whether the priming tasks involved auditory or visual presentation. Inserting a 1-h delay between the priming tasks and the syllable-in-noise task, the benefits persisted but were confined to primed syllable pairs. Finally, we demonstrated the effectiveness of this approach in older adults. Our findings substantiate the existence of a speech production-perception relationship. They also have clinical relevance as they raise the possibility of production-based interventions to improve speech perception ability. This would be particularly relevant for older adults who often encounter difficulties in perceiving speech in noise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".