Using spatial release from masking to estimate the magnitude of the familiar-voice intelligibility benefit
Bibliographic record
Abstract
The ability to segregate simultaneous speech streams is crucial for successful communication. Recent studies have demonstrated that participants can report 10%-20% more words spoken by naturally familiar (e.g., friends or spouses) than unfamiliar talkers in two-voice mixtures. This benefit is commensurate with one of the largest benefits to speech intelligibility currently known-that which is gained by spatially separating two talkers. However, because of differences in the methods of these previous studies, the relative benefits of spatial separation and voice familiarity are unclear. Here, the familiar-voice benefit and spatial release from masking are directly compared, and it is examined if and how these two cues interact with one another. Talkers were recorded while speaking sentences from a published closed-set "matrix" task, and then listeners were presented with three different sentences played simultaneously. Each target sentence was played at 0° azimuth, and two masker sentences were symmetrically separated about the target. On average, participants reported 10%-30% more words correctly when the target sentence was spoken in a familiar than unfamiliar voice (collapsed over spatial separation conditions); it was found that participants gain a similar benefit from a familiar target as when an unfamiliar voice is separated from two symmetrical maskers by approximately 15° azimuth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".