Familiar Voices Are More Intelligible, Even if They Are Not Recognized as Familiar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We can recognize familiar people by their voices, and familiar talkers are more intelligible than unfamiliar talkers when competing talkers are present. However, whether the acoustic voice characteristics that permit recognition and those that benefit intelligibility are the same or different is unknown. Here, we recruited pairs of participants who had known each other for 6 months or longer and manipulated the acoustic correlates of two voice characteristics (vocal tract length and glottal pulse rate). These had different effects on explicit recognition of and the speech-intelligibility benefit realized from familiar voices. Furthermore, even when explicit recognition of familiar voices was eliminated, they were still more intelligible than unfamiliar voices-demonstrating that familiar voices do not need to be explicitly recognized to benefit intelligibility. Processing familiar-voice information appears therefore to depend on multiple, at least partially independent, systems that are recruited depending on the perceptual goal of the listener.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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