Voice familiarity helps infants tackle variability in the speech signal.
Bibliographic record
Abstract
The acoustic realization of lexical items produced by different speakers can vary greatly. Current research suggests that infants, unlike adults, struggle to cope with this lack of invariance in the realization of words. Although 7.5-month olds are able to recognize words across different utterances when produced by speakers of the same gender with similar voices, they fail to do so when target words produced in a female voice are subsequently produced in a male voice [Houston and Jusczyk (2000)]. Note that all work in this area has used disembodied unfamiliar voices to test infants. In the current study, we ask whether infants might perform better under more ecologically valid conditions, i.e., when tested on familiar rather than unfamiliar voices. Using the headturn preference procedure, infants were familiarized with passages spoken by their mother. During the test phase, they were presented with their father’s voice producing isolated repetitions of familiarized target words. Preliminary results suggest that infants may recognize words across different utterances produced by speakers of different genders if they are highly familiar with both the male and female speakers. In other words, infants may handle variability in the realization of words better when tested on familiar rather than unfamiliar voices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".