Effect of Age on F<sub>0</sub>Difference Limen and Concurrent Vowel Identification
Bibliographic record
Abstract
PURPOSE: To investigate the effect of age on voice fundamental frequency (F0) difference limen (DL) and identification of concurrently presented vowels. METHOD: Fifteen younger and 15 older adults with normal audiometric thresholds in the speech range participated in 2 experiments. In Experiment 1, F0 DLs were measured for a synthesized vowel. In Experiment 2, accuracy in identifying concurrently presented vowel pairs was measured. Vowel pairs were formed from 5 synthesized vowels with F0 separations ranging from 0 to 4 semitones. RESULTS: Younger adults had smaller (better) F0 DLs than older adults. For the older group, age was significantly correlated with F0 DLs. Younger adults identified concurrent vowels more accurately than older adults. When the vowels in the pairs had different formants, both age groups benefited similarly from F0 separation. Interestingly, when both constituent vowels had identical formants, F0 separation was deleterious, especially for older adults. Pure-tone average threshold did not correlate significantly with either F0 DL or accuracy in concurrent vowel identification. CONCLUSION: Age-related declines were confirmed for F0 DLs, identification of concurrently spoken vowels, and benefit from F0 separation between vowels with identical formants. This pattern of findings is consistent with age-related deficits in periodicity coding.
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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 | 0.004 |
| 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 | 0.000 |
| 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".