Vocal and articulatory performance fluctuates across the menstrual cycle: Acoustic analysis of daily single participant speech data over three months
Bibliographic record
Abstract
The impact of sex hormones on voice and speech has been observed across the menstrual cycle; however, the evidence remains mixed. Here, we investigated voice and articulatory performance as a function of the menstrual cycle over three months via an intensive longterm single case experimental design. A 17-yearold naturally cycling female who did not use oral/hormonal contraceptives gave a written consent to participate in the study over a period of three cycles (i.e., 90 days). Daily urine sample test strips were utilized to identify the onset of ovulation (via luteinizing hormone, LH). Daily voice and articulation tasks including a Maximum Phonation Time (MPT) task, a diadochokinesis (DDK) task were completed. A harmonic regression analysis showed a linear learning effect for DDK rate (p<.00001) with a fluctuation of values as a function of the menstrual cycle (p = 0.024). MPT showed a cyclic fluctuation (p = 0.028) but no linear learning effect. Linear and harmonic terms for fundamental frequency (measured from MPT recordings) showed no significant patterns. Understanding hormone driven variability in speech and voice helps to inform remediation strategies that aim to minimize the impact of speech motor and voice disorders across the lifespan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".