Reduced coarticulation and aging
Bibliographic record
Abstract
Lifespan changes in speech have mostly been documented with respects to children’s development, while little is known about its evolution throughout adulthood. More particularly our knowledge on the effect of aging on speech and voice is sparse. Changes have been found in voice quality [e.g., Xue & Hao, 2003], the speech rate has been described to slow down [e.g., Staiger et al. 2017), and pitch is said to raise for older males and to lower for older females [e.g., Harnsberger et al., 2008]. The present study aims to investigate the effect of aging on Vowelto_Vowel anticipatory coarticulation in French. This effect is tested according to vowel duration and to differences in regional varieties. Data from 240 speakers (half female) distributed across three age groups (20–45), (50–69), and (70–80) have been extracted from the MonPaGeHA database [Fougeron et al. 2018] which includes speakers from four regional varieties: France, Belgium, Switzerland, and Quebec. The influence of V2 (/a/ or /i/) on V1 (/a/) is measured as a lowering of F1 and a rise of F2. Results show that coarticulation and vowel duration varies with regional variety and that vowels of older speakers are lengthened. More interestingly, results show a reduction of coarticulation 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".