Intelligibility benefit for familiar voices does not depend on better discrimination of fundamental frequency or vocal tract length
Bibliographic record
Abstract
Speech is more intelligible when it is spoken by familiar than unfamiliar people. Two cues to voice identity are glottal pulse rate (GPR) and vocal tract length (VTL): perhaps these features are more accurately represented for familiar voices in a listener’s brain. If so, listeners should be able to discriminate smaller manipulations to perceptual correlates of these vocal parameters for familiar than unfamiliar voices. We recruited pairs of friends who had known each other for 0.5–22.5 years. We measured thresholds for discriminating pitch (correlate of GPR) and formant spacing (correlate of VTL; ‘VTL-timbre’) for voices that were familiar (friends) and unfamiliar (friends of other participants). When a competing talker was present, speech was substantially more intelligible when it was spoken in a familiar voice. Discrimination thresholds were not systematically smaller for familiar compared to unfamiliar talkers. Although, participants detected smaller deviations to VTL-timbre than pitch uniquely for familiar talkers, suggesting a different balance of characteristics contribute to discrimination of familiar and unfamiliar voices. Across participants, we found no relationship between the size of the intelligibility benefit for a familiar over an unfamiliar voice and the difference in discrimination thresholds for the same voices. Also, the intelligibility benefit was not affected by the acoustic manipulations we imposed on voices to assess discrimination thresholds. Overall, these results provide no evidence that two important cues to voice identity—pitch and VTL-timbre—are more accurately represented when voices are familiar, or are necessarily responsible for the large intelligibility benefit derived from familiar voices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".