Factors Affecting Lay Persons’ Identification Of Speakers
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A perpetrator speaking over the telephone or one whose face was obscured or disguised are examples of incidents that might lead to testimony on voice identification. Earwitness identification is part of the general area of person identification, but refers specifically to victims' and witnesses' verbal descriptions of voices and speaker identification. Although many laypersons give significantly more credibility to the identification of speakers than is justified, experts generally agree that earwitness descriptions and identification should be treated by the criminal justice system with great caution. This article presents a scientific overview of factors that affect the accuracy of speaker identification, or what is referred to as aural-perceptual analysis, and discusses the reliability and validity of speaker recognition and identification. The police do not have the luxury of handpicking their witnesses (or culprits) but must interview any and all male and female victims or witnesses, all of whom can differ in age, race, expertise, and other characteristics. The article also considers showups and voice lineups.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it