Aging voices and speech intelligibility: Implications for communication by older talkers
Bibliographic record
Abstract
There are various changes in the voice production system during the course of normal aging. While some older adults have voices that are indistinguishable from those of younger adults, other older adults have perceptibly poorer voices that are characterized by irregularities in fundamental frequency and intensity. Previous studies have demonstrated that listeners perform worse on speech-in-noise tests when speech stimuli are distorted by temporal jittering. In this study, we tested whether natural jitter in the voices of older talkers affected the intelligibility of their speech when it was presented in noise. We selected three older female talkers from a larger pool of older female adults based on several voice acoustic measures, including jitter and harmonics-to-noise ratio; one talker had a relatively poor voice, one talker had an average voice and one talker had a good voice. These talkers recorded stimuli from the NU Auditory Test No. 6, which were then presented to young adult listeners in +1 dB SNR multi-talker babble noise. Surprising, the results showed that the talker with the average voice was the least intelligible of the three older talkers, while the talker with the poorest voice was as intelligible as the talker with the best voice. This pattern of results was reproduced after equating target word intensities across all talkers. Preliminary acoustic analyses showed that the three talkers produced vowels of similar duration and intensity but that the talker with the poorest voice had the slowest speech rate and the longest consonant durations. These findings suggest that older adults with poorer voices may alter their speech behaviour to maintain communication effectiveness despite natural declines in voice quality.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".