The Influence of Pitch and Loudness Changes on the Acoustics of Vocal Tremor
Bibliographic record
Abstract
The effect of tremor on phonation is to modulate an otherwise steady sound source in its amplitude, fundamental frequency, or both. The severity of untreated vocal tremor has been reported to change under certain conditions that may be related to muscle tension. In order to better understand the phenomenon of vocal tremor, its acoustic properties were examined as individuals volitionally altered their pitch and loudness. These voice conditions were anticipated to alter the tension of the intrinsic laryngeal muscles. The voices of 10 individuals with a diagnosis of vocal tremor were recorded before participating in a longitudinal treatment study. They produced vowels at low and high pitch and loudness levels as well as in a comfortable voice condition. Acoustic analyses quantified the amplitude and frequency modulations of the speakers' voices across the various conditions. Individual speakers varied in the way the pitch and loudness changes affected their tremor, but the following statistically significant effects for the speakers as a group were observed: Higher pitch phonation was associated with a more rapid rate for both amplitude and frequency modulations. Amplitude modulation become faster for louder phonation. Low-pitched phonotion led to decreases in the extent of amplitude tremor. Varying pitch led to dramatic changes in the phase relationship between amplitude and frequency modulation in some of the speakers, whereas this effect was not apparent in other speakers.
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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 | 0.004 |
| 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) | 0.002 | 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".