Infant-directed speech enhances recognizability of individual mothers’ voices
Bibliographic record
Abstract
Adult speakers commonly alter their voices when talking to infants, giving rise to an infant-directed speech (IDS) style. Here we tested the effects of infant-directed speech on the recognizability of a speaker’s voice. 10 Swiss-German mothers were recorded talking to their infants IDS and talking to an adult experimenter (in adult-directed speech, ADS). We studied the indexical properties using Mel-frequency cepstral coefficients (MFCCs). By using an unsupervised K-means clustering algorithm, the segmental 13-dimensional MFCCs were clustered and reduced to two dimensions using Principal Component Analysis. Results showed that the relative area of IDS occupied in the 13-dimensional space was significantly larger compared to the area occupied by ADS. A supervised language-independent Gaussian Mixture Model revealed that this expansion benefitted the recognizability of mothers’ voices. This means that the higher indexical variability in IDS fosters recognition of individual mothers. Results are consistent with the view that IDS may have evolved in part as a strategy to promote indexical signaling by a mother to her offspring, thereby promoting mother-infant attachment and fostering offspring survival.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".