Common-sense beliefs, recognition and the identification of familiar and unfamiliar speakers from verbal and non-linguistic vocalizations
Bibliographic record
Abstract
Listeners first listened to tape-recorded speech samples of words and non-linguistic vocalizations to decide whether each came from a familiar or an unfamiliar speaker. If a vocalization was judged as being uttered by a familiar speaker, listeners were asked to identify, if possible, the individual by name. Listeners were instructed not to guess. Confidence-accuracy scores in judgements and identification also were assessed. A separate group of participants attempted to predict the accuracy of performance of the listeners for each of the utterances made by the familiar and unfamiliar speakers. Tokens of some utterances, namely the words hello and help me and the sounds of laughter and clearing the throat, allowed equally good performance on the familiar/unfamiliar decision whether their source was a familiar or an unfamiliar speaker. For other vocalizations (moan, cough, grunt and sigh) listeners were better at detecting unfamiliar speakers than at recognizing familiar speakers. Those utterances judged to be made by familiar speakers led to correct identifications approximately 50 percent of the time on hearing the words hello and help me, and laughter. All other utterances led to less than 30 percent correct speaker identifications. For those unfamiliar speakers who were falsely recognized as familiar, listeners falsely identified by name between 22 and 30 percent of the vocalizations. Few of the confidence-accuracy relationships were significant. Laypersons’ common-sense beliefs for speaker identification were generally unrealistic.
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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 | 0.006 | 0.041 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".