Bibliographic record
Abstract
Human adults rely on both acoustic and linguistic information to identify adult talkers. Assuming favorable conditions, adult listeners recognize other adults fairly accurately and quickly. But how well can adult listeners recognize child talkers, whose speech productions often differ dramatically from adult speech productions? Although adult talker recognition has been heavily studied, only one study to date has directly compared the recognition of unfamiliar adult and child talkers [Creel and Jimenez (2012). J. Exp. Child Psychol. 113(4), 487-509]. Therefore, the current study revisits this question with a much larger and younger sample of child talkers (N = 20); performance with adult talkers (N = 20) was also tested to provide a baseline. In Experiment 1, adults successfully distinguished between adult talkers in an AX discrimination task but performed much worse with child talkers. In Experiment 2, adults were slower and less accurate at learning to identify child talkers than adult talkers in a training-identification task. Finally, in Experiment 3, adults failed to improve at identifying child talkers after three days of training with numerous child voices. Taken together, these findings reveal a sizable difference in adults' ability to recognize child versus adult talkers. Possible explanations and implications for understanding human talker recognition are discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".