Infant–Mother Acoustic–Prosodic Alignment and Developmental Risk
Bibliographic record
Abstract
Purpose: One promising early marker for autism and other communicative and language disorders is early infant speech production. Here we used daylong recordings of high- and low-risk infant-mother dyads to examine whether acoustic-prosodic alignment as well as two automated measures of infant vocalization are related to developmental risk status indexed via familial risk and developmental progress at 36 months of age. Method: Automated analyses of the acoustics of daylong real-world interactions were used to examine whether pitch characteristics of one vocalization by the mother or the child predicted those of the vocalization response by the other speaker and whether other features of infants' speech in daylong recordings were associated with developmental risk status or outcomes. Results: Low-risk and high-risk dyads did not differ in the level of acoustic-prosodic alignment, which was overall not significant. Further analyses revealed that acoustic-prosodic alignment did not predict infants' later developmental progress, which was, however, associated with two automated measures of infant vocalizations (daily vocalizations and conversational turns). Conclusions: Although further research is needed, these findings suggest that automated measures of vocalizations drawn from daylong recordings are a possible early identification tool for later developmental progress/concerns. Supplemental Material: https://osf.io/cdn3v/.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".