Effects of age on speech and voice quality ratings
Bibliographic record
Abstract
The quality of communication may be affected by listeners' perception of talkers' characteristics. This study examined if there were effects of talker and listener age on the perception of speech and voice qualities. Younger and older listeners judged younger and older talkers' gender and age, then rated speech samples on pleasantness, naturalness, clarity, ease of understanding, loudness, and the talker's suitability to be an audiobook reader. For the same talkers, listeners also rated voice samples on pleasantness, roughness, and power. Younger and older talkers were perceived to be similar on most qualities except age. Younger and older listeners rated talkers similarly, except that younger listeners perceived younger voices to be more pleasant and less rough than older voices. For vowel samples, younger listeners were more accurate than older listeners at age estimation, while older listeners were more accurate than younger listeners at gender identification, suggesting that younger and older listeners differ in their evaluation of specific talker characteristics. Thus, the perception of quality was generally more affected by the age of the listener than the age of the talker, and age-related differences between listeners depended on whether voice or speech samples were used and the rating being made.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".