Effects of voice familiarity and age on perceptual organization of sound from two competing talkers
Bibliographic record
Abstract
We used the Coordinate Response Measure (CRM) procedure (Bolia et al, 2000) to examine whether a non-acoustic characteristic of speech -voice familiarity- can affect a listener's ability to separate competing voices. We tested 27 listeners, aged 45-79, with their spouse's and two novel voices (other listeners' spouses). Couples had been living together at least 18 years. On each trial, two different talkers produced two of four callsigns (one being the target 'Baron'), two of four colours and two of eight numbers, and the participant responded by indicating the colour-number combination to which 'Baron' was told to go. We tested three conditions: spouse-voice target phrase with a novel-voice masker phrase (F/N); novel-voice target with spouse-voice masker (N/F); novel-voice target with (different) novel-voice masker (N/N). Compared to N/N, performance was significantly better in F/N, and, crucially, N/F conditions, indicating that listeners can exploit learned characteristics of a masking voice to help them track a novel voice. Furthermore, whereas the younger participants (under 60) benefited from having a familiar voice as target or as masker, the older group (over 60) benefited only when the familiar voice was the focus of attention, suggesting that the ability to use voice familiarity to segregate sounds changes with age.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".