Effect of F0 on the intelligibility of emotional speech in noise for younger and older listeners
Bibliographic record
Abstract
This study investigated if F0 variability could explain why intelligibility in noise is better for speech spoken to portray fear compared to emotionally neutral speech. Word recognition accuracy was measured for stimuli produced with neutral vocal emotion, for intact stimuli portraying fear, and for six versions of the fear stimuli with varying reductions in F0 variability. Younger and older adults were tested in speech-spectrum noise and two-talker babble. Younger adults outperformed older adults. As F0 variability in the fear stimuli was reduced, performance in speech-spectrum noise for both age groups decreased and approached performance for the neutral stimuli. In the two-talker babble, even when F0 variability was most reduced, performance remained higher for the fear stimuli than the neutral stimuli, especially for older adults. The mean F0 for the fear stimuli was higher than for the neutral stimuli, while mean F0 of the neutral stimuli and the two-talker masker were similar. Thus, although the greater F0 variability in the fear stimuli confers an advantage to both age groups in speech-spectrum noise, F0 mean and variability both contribute to the effect of emotion on intelligibility in two-talker babble, especially for older listeners.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".