Effects of a consistent target or masker voice on target speech intelligibility in two- and three-talker mixtures
Bibliographic record
Abstract
When the spatial location or identity of a sound is held constant, it is not masked as effectively by competing sounds. This suggests that experience with a particular voice over time might facilitate perceptual organization in multitalker environments. The current study examines whether listeners benefit from experience with a voice only when it is the target, or also when it is a masker, using diotic presentation and a closed-set task (coordinate response measure). A reliable interaction was observed such that, in two-talker mixtures, consistency of masker or target voice over 3-7 trials significantly benefited target recognition performance, whereas in three-talker mixtures, target, but not masker, consistency was beneficial. Overall, this work suggests that voice consistency improves intelligibility, although somewhat differently when two talkers, compared to three talkers, are present, suggesting that consistent-voice information facilitates intelligibility in at least two different ways. Listeners can use a template-matching strategy to extract a known voice from a mixture when it is the target. However, consistent-voice information facilitates segregation only when two, but not three, talkers are present.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".