Speaker matters: Natural inter-speaker variation affects 4-month-olds’ perception of audio-visual speech
Bibliographic record
Abstract
In the language development literature, studies often make inferences about infants’ speech perception abilities based on their responses to a single speaker. However, there can be significant natural variability across speakers in how speech is produced (i.e., inter-speaker differences). The current study examined whether inter-speaker differences can affect infants’ ability to detect a mismatch between the auditory and visual components of vowels. Using an eye-tracker, 4.5-month-old infants were tested on auditory-visual (AV) matching for two vowels (/i/ and /u/). Critically, infants were tested with two speakers who naturally differed in how distinctively they articulated the two vowels within and across the categories. Only infants who watched and listened to the speaker whose visual articulations of the two vowels were most distinct from one another were sensitive to AV mismatch. This speaker also produced a visually more distinct /i/ as compared to the other speaker. This finding suggests that infants are sensitive to the distinctiveness of AV information across speakers, and that when making inferences about infants’ perceptual abilities, characteristics of the speaker should be taken into account.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".