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-speaking participants who were friends or romantic couples. Participants reported words from closed-set English sentences (i.e., Oldenburg Matrix Test; Zokoll et al., 2013) spoken by a familiar talker (the participant's partner) or an unfamiliar talker. We compared 3 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. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".