Speech spoken by familiar people is more resistant to interference by linguistically similar speech
Bibliographic record
Abstract
Understanding speech in adverse conditions is affected by experience—a familiar voice is substantially more intelligible than an unfamiliar voice when competing speech is present, even if the content of the speech (the words) are controlled. This familiar-voice benefit is observed consistently, but its underpinnings are unclear: Do familiar voices simply attract more attention, are they inherently more intelligible because they have predictable acoustic characteristics, or are they more intelligible in a mixture because they are more resistant to interference from other sounds? We recruited pairs of native English participants who were friends or romantic couples. Participants reported words from closed-set English sentences (Oldenburg matrix test; HörTech, 2014) spoken by a familiar talker (the participant’s partner) or an unfamiliar talker. We compared three masker conditions that are acoustically similar but differ in their demands: (1) English Oldenburg sentences; (2) Oldenburg sentences in a language incomprehensible to the listener (Russian or Spanish); and (3) unintelligible signal-correlated noise. We adaptively varied the target-to-masker ratio to obtain 50% speech reception thresholds. We observed a large (~5 dB) familiar-voice benefit when the target and masker were both English sentences. This benefit was attenuated (to ~2 dB) when the masker was in an incomprehensible language and disappeared when it was signal-correlated noise. These results suggest that familiar voices did not benefit intelligibility because they were more predictable or because they attracted greater attention; rather, familiarity with a target voice reduced interference from maskers that are linguistically similar to the target.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".