Distinguishing Dick from Jane: Children's voices are more difficult to identify than adults' voices
Bibliographic record
Abstract
Previous research on talker recognition has focused on the processing of talker identity in adult voices; however, little is known about how listeners identify child voices. This study examined adult listeners’ ability to differentiate and identify child voices (2.5-year-olds) and adult voices. In Experiment 1, native English-speaking listeners completed an AX voice discrimination task with both child and adult voices. Results revealed that listeners were significantly worse at discriminating between child voices relative to adult voices, regardless of the fact that child voices were mixed gender and adult voices were all female. In Experiment 2, we examined whether listeners could learn to identify child and adult voices. Adult listeners completed an adaptive training paradigm, where they learned to identify 4 child voices on one day and 4 adult voices on a separate day. Preliminary results indicate that with training, listeners can learn to identify voices from both age groups above chance. However, listeners were still faster at learning and more accurate at identifying adult relative to child voices. Taken together, these findings are an initial step towards understanding how talker recognition is influenced by the acoustic-phonetic characteristics and articulatory capabilities that vary with talker age.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".