Audiovisual speech perception in children: a scoping review
Bibliographic record
Abstract
This scoping review provides a descriptive synthesis of available evidence on children's audiovisual speech perception. We used eight databases to identify the experimental studies published 2000–2019, and reported the data using the guidelines of PRISMA-ScR designed for scoping reviews. While research conducted prior to 2000 provided a strong foundation in this area, the past two decades have brought technical advances that have allowed for more precise measurement of audiovisual speech perception. Thirty-eight studies were identified: 18 articles that focused on children with typical development, 9 focused on children with autism spectrum disorder, 8 focused on children with speech and language disorders, and 3 focused on children with hearing loss. Most of the studies identified were behavioral studies, while a minority reported on neuroanatomical correlates underlying the audiovisual speech perception. Through this scoping review, key gaps were identified that include few studies in clinical populations, a few studies on languages other than English, and variability in terminology to describe similar or overlapping concepts. Further research is needed to inform the development and mechanisms of audiovisual speech integration in children with different language development paths. In addition, the use of common terminology in future research would improve access to evidence and the communication of this knowledge for researchers and clinicians.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".