The benefit to speech intelligibility of hearing a familiar voice
Bibliographic record
Abstract
Previous experience with a voice can help listeners understand speech when a competing talker is present. Using the Coordinate-Response Measure (CRM) task (Bolia, 2000), Johnsrude et al. (2013) demonstrated that speech is more intelligible when either the target or competing (masking) talker is a long-term spouse than when both talkers are unfamiliar (termed ‘familiar-target’ and ‘familiar-masker’ benefits, respectively). To better understand how familiarity improves intelligibility, we measured the familiar-target and familiar-masker benefits in older and younger spouses using a more challenging matrix task, and compared the benefits listeners gain from spouses’ and friends’ voices. On each trial, participants heard two sentences from the Boston University Gerald (Kidd et al., 2008) corpus (“ ”) and reported words from the sentence beginning with a target name word. A familiar-masker benefit was not observed, but all groups showed a robust familiar-target benefit and its magnitude did not differ between spouses and friends. The familiar-target benefit was not influenced by relationship length (in the range of 0.5–52 years). Together, these results suggest that the familiar-target benefit can develop from various types of relationships and that it reaches a ceiling within several months of meeting a new friend or partner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".