Investigating Speech Tempo, Speaking Rate, and the Related Factors in the Iranian Elderly Women Talking with Tehrani and Semnani Accents
Bibliographic record
Abstract
Background: Some variables, such as age, gender, regional and dialectical differences influence speech tempo. Men and younger individuals speak faster than women and the elderly. Therefore, these variations should be considered when assessing speaking rate. Objectives: Since different accents influence speaking rates and there is no previous study investigated speech tempo with respect to regional and accent differences in Iran, and given that the elderly are more prone to problems influencing speaking rate, the present study was done to compare speech tempo and speaking rate in two different accents, namely Tehrani and Semnani, and to investigate some related factors. Methods: This cross-sectional study was performed on 200 elderly women selected via convenience sampling method. Speech tempo, speaking rate, verbal fluency, and cognition scores were compared using an independent-samples t-test. Pearson’s correlation coefficient test was used to assess correlations between speaking rate and level of education, Montreal cognitive assessment (MoCA), and verbal fluency scores. Results: No significant difference was found in speech tempo between the studied accents (P = 0.13). Speaking rate was significantly slower in the Tehrani accent than the Semnani one (P = 0.04). The Tehrani elderly obtained significantly less scores in verbal fluency and MoCA ((P ≤ 0.001) and (P = 0.04), respectively. In both groups, speaking rate had a significant correlation with verbal fluency and MoCA scores but not with level of education. Conclusions: Although, our results showed no difference in speech tempo between the studied accents, the Tehrani elderly unexpectedly spoke more slowly meaning that they paused more while speaking. There was a relationship between faster speaking, better verbal fluency, and cognitive performance.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".