Acoustic variability affects asymmetry in infant speech discrimination
Bibliographic record
Abstract
From birth, infants are capable of discriminating speech sounds that occur cross-linguistically, but by their first year, infants have difficulty discriminating most non-native contrasts (Werker and Tees, 1984). Yet, language experience is not the only factor affecting discrimination. Further research into language-specific perception reveals asymmetries occur in the discrimination of vowels according to perceptual space (Polka and Bohn, 2003) and consonants based on frequency in the input (Anderson etal., 2003). It might be that acoustic variability also causes asymmetries in infant speech perception. Seventy-six 6- and 9-month olds participated in a discrimination task comparing bilabial and velar stop - /l/ onsets to unattested coronal stop - /l/ onsets (e.g. /kla/ - /tla/; /pla/ - /tla/) in both voiced and voiceless conditions. Infants successfully discriminated coronal from bilabial onsets (p<0.05), but not velar (p>0.05), with no effects of age or voicing. Adult productions of the attested clusters were subjected to an acoustic analysis. Of the four tested characteristics, higher standard deviations were found in velar onsets (/kla/ > /pla/: F2 onset and liquid duration; /gla/ > /bla/: F2 slope, VOT, and liquid duration). This suggests that acoustic variability in the input affects infants' speech discrimination.
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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 | 0.003 |
| 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".