Vowel space and variability in infant- and adult-directed speech
Bibliographic record
Abstract
Infant- and adult-directed speech are acoustically-distinct registers, but whether the characteristics of infant-directed speech (IDS) promote speech category learning is unclear. Several studies have reported an expanded vowel space in IDS compared to ADS; point vowels (/i/, /a/, /u/) are, on average, more distinct from each other in IDS. But, other studies report greater intra-category variability which might diminish any benefits of an expanded vowel space. Here, we examined vowel productions across five vowels as mothers spoke to their infants (7- or 15-months) in IDS and an adult experimenter in ADS. We observed an expanded point vowel space and an increase in variability of within-category vowel productions in IDS toward 15-month-olds, compared to ADS. Thus, although the centroids of the point vowels were more separated in IDS toward 15-month-olds than ADS, production variability led to substantial category overlap. Yet, classification modeled using Discriminant Function Analysis (DFA) revealed near-ceiling classification rates for both registers, indicating that there was no classification advantage of the increased distance among IDS point vowels. We found neither vowel space expansion nor increased variability in IDS toward 7-month-olds. These findings inform how distributional characteristics of speech input may contribute to vowel category learning in infancy.
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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 | 0.000 | 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 | 0.001 | 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".