The development of multisensory speech perception continues into the late childhood years
Bibliographic record
Abstract
Observing a speaker's articulations substantially improves the intelligibility of spoken speech, especially under noisy listening conditions. This multisensory integration of speech inputs is crucial to effective communication. Appropriate development of this ability has major implications for children in classroom and social settings, and deficits in it have been linked to a number of neurodevelopmental disorders, especially autism. It is clear from structural imaging studies that there is a prolonged maturational course within regions of the perisylvian cortex that persists into late childhood, and these regions have been firmly established as being crucial to speech and language functions. Given this protracted maturational timeframe, we reasoned that multisensory speech processing might well show a similarly protracted developmental course. Previous work in adults has shown that audiovisual enhancement in word recognition is most apparent within a restricted range of signal-to-noise ratios (SNRs). Here, we investigated when these properties emerge during childhood by testing multisensory speech recognition abilities in typically developing children aged between 5 and 14 years, and comparing them with those of adults. By parametrically varying SNRs, we found that children benefited significantly less from observing visual articulations, displaying considerably less audiovisual enhancement. The findings suggest that improvement in the ability to recognize speech-in-noise and in audiovisual integration during speech perception continues quite late into the childhood years. The implication is that a considerable amount of multisensory learning remains to be achieved during the later schooling years, and that explicit efforts to accommodate this learning may well be warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".