Familiarity and task context shape the use of acoustic information in voice identity perception
Bibliographic record
Abstract
Familiar and unfamiliar voice perception are often understood as being distinct from each other. For identity perception, theoretical work has proposed that listeners use acoustic information in different ways to perceive identity from familiar and unfamiliar voices: Unfamiliar voices are thought to be processed based on close comparisons of acoustic properties, while familiar voices are processed based on diagnostic acoustic features that activate a stored person-specific representation of that voice. To date no empirical study has directly examined whether and how familiar and unfamiliar listeners differ in their use of acoustic information for identity perception. Here, we tested this theoretical claim by linking listeners' judgements in voice identity tasks to complex acoustic representation - spectral similarity of the heard voice recordings. Participants (N = 177) who were either familiar or unfamiliar with a set of voices completed an identity discrimination task (Experiment 1) or an identity sorting task (Experiment 2). In both experiments, identity judgements for familiar and unfamiliar voices were guided by spectral similarity: Pairs of recordings with greater acoustic similarity were more likely to be perceived as belonging to the same voice identity. However, while there were no differences in how familiar and unfamiliar listeners used acoustic information for identity discrimination, differences were apparent for identity sorting. Our study therefore challenges proposals that view familiar and unfamiliar voice perception as being at all times distinct. Instead, our data suggest a critical role of the listening situation in which familiar and unfamiliar voices are evaluated, thus characterising voice identity perception as a highly dynamic process in which listeners opportunistically make use of any kind of information they can access.
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How this classification was reachedexpand
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 | 0.003 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".