Examining the relationship between the interpretation of age and gender across languages.
Bibliographic record
Abstract
Speech signals vary substantially in a number of their key properties, with the variability deriving from, among other things, talkers' age and gender. Speech processing requires resolution of this variation, necessitating interpretation of age and gender information in the signal. In some signals, the age and gender are not clear from acoustic information alone. In these cases there may be substantial individual variation in judgments of age and gender. This study examined the interplay between the interpretation of age and gender across language communities. Corner vowel stimuli ([i], [u], [a]) generated by an age-varying articulatory synthesizer set at seven different ages (6 months, 2, 4, 5, 10, 16, and 21 years) were presented to native speakers of Cantonese, English, and Japanese. Listeners assigned an age (in years) and a gender (along a visual analog scale ranging from "definitely male" to "definitely female", or the equivalent in Japanese or Cantonese) to each stimulus. Analysis revealed a bifurcation in the interpretation of age and gender for the age 10 stimuli, which subjects rated as either a younger male or older female, suggesting a nonuniformity in the resolution of variability during speech processing. Preliminary analysis further suggests that this nonuniformity may be culture-specific.
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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 | 0.008 |
| 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 | 0.001 |
| 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".