Auditory-Visual Integration for Speech by Children With and Without Specific Language Impairment
Bibliographic record
Abstract
PURPOSE: It has long been known that children with specific language impairment (SLI) can demonstrate difficulty with auditory speech perception. However, speech perception can also involve the integration of both auditory and visual articulatory information. METHOD: Fifty-six preschool children, half with and half without SLI, were studied in order to examine auditory-visual integration. Children watched and listened to video clips of a woman speaking [bi] and [gi]. They also listened to audio clips of [bi], [di], and [gi], produced by the same woman. The effect of visual input on speech perception was tested by presenting an auditory [bi] combined with a visually articulated [gi], which tends to alter the phoneme percept (the McGurk effect). RESULTS: Both groups of children performed at ceiling when asked to identify speech tokens in auditory-only and congruent auditory-visual modalities. In the incongruent auditory-visual condition, a stronger McGurk effect was found for the normal language group compared with the children with SLI. CONCLUSION: Responses by the children with SLI indicated less impact of visual processing on speech perception than was seen with their normal peers. These results demonstrate that the difficulties with speech perception by SLI children extend beyond the auditory-only modality to include auditory-visual processing as well.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".