When infants talk, infants listen: pre‐babbling infants prefer listening to speech with infant vocal properties
Bibliographic record
Abstract
To learn to produce speech, infants must effectively monitor and assess their own speech output. Yet very little is known about how infants perceive speech produced by an infant, which has higher voice pitch and formant frequencies compared to adult or child speech. Here, we tested whether pre-babbling infants (at 4-6 months) prefer listening to vowel sounds with infant vocal properties over vowel sounds with adult vocal properties. A listening preference favoring infant vowels may derive from their higher voice pitch, which has been shown to attract infant attention in infant-directed speech (IDS). In addition, infants' nascent articulatory abilities may induce a bias favoring infant speech given that 4- to 6-month-olds are beginning to produce vowel sounds. We created infant and adult /i/ ('ee') vowels using a production-based synthesizer that simulates the act of speaking in talkers at different ages and then tested infants across four experiments using a sequential preferential listening task. The findings provide the first evidence that infants preferentially attend to vowel sounds with infant voice pitch and/or formants over vowel sounds with no infant-like vocal properties, supporting the view that infants' production abilities influence how they process infant speech. The findings with respect to voice pitch also reveal parallels between IDS and infant speech, raising new questions about the role of this speech register in infant development. Research exploring the underpinnings and impact of this perceptual bias can expand our understanding of infant language development.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".