Effects of acoustic variability on infant speech perception
Bibliographic record
Abstract
From birth, infants are capable of discriminating many of the speech sounds that occur cross-linguistically [Werker and Tees (1984)]. However, there are cases where the direction of presentation of contrasts reveals asymmetries in the discrimination of some speech contrasts, suggesting that some phonetic categories are more salient than others. These asymmetries may be due to an inherently uneven perceptual space [Polka and Bohn (2011)] or shaped by distributional properties of the input [Anderson et al. (2003)]. We explore whether acoustic variability also cause perceptual asymmetries. Six- and 9-month-olds participated in a discrimination task comparing English legal and illegal stop-liquid onsets (e.g., /kla/-/tla/ & /pla/-/tla/). Infants discriminated coronal versus bilabial onsets (p < 0.05), but not coronal versus velar (p > 0.05). Analysis of adult productions revealed that velar stop-liquid onsets showed more variability in their production, suggesting acoustic variability affects infants’ perception. The current study provides a more direct test of the hypothesis that acoustic variability drives perceptual biases. Using the same clusters, we are exploring 9-month-olds’ directional asymmetries. Preliminary results show that 9-month-olds successfully discriminate /dla/ after familiarization to /bla/ (p = 0.05; n = 5). Further investigation will reveal whether presentation direction affects infants’ sensitivity to acoustic variability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".