The influence of vocal training and acting experience on measures of voice quality and emotional genuineness
Bibliographic record
Abstract
Vocal training through singing and acting lessons is known to modify acoustic parameters of the voice. While the effects of singing training have been well documented, the role of acting experience on the singing voice remains unclear. In two experiments, we used linear mixed models to examine the relationships between the relative amounts of acting and singing experience on the acoustics and perception of the male singing voice. In Experiment 1, 12 male vocalists were recorded while singing with five different emotions, each with two intensities. Acoustic measures of pitch accuracy, jitter, and harmonics-to-noise ratio (HNR) were examined. Decreased pitch accuracy and increased jitter, indicative of a lower “voice quality,” were associated with more years of acting experience, while increased pitch accuracy was associated with more years of singing lessons. We hypothesized that the acoustic deviations exhibited by more experienced actors was an intentional technique to increase the genuineness or truthfulness of their emotional expressions. In Experiment 2, listeners rated vocalists’ emotional genuineness. Vocalists with more years of acting experience were rated as more genuine than vocalists with less acting experience. No relationship was reported for singing training. Increased genuineness was associated with decreased pitch accuracy, increased jitter, and a higher HNR. These effects may represent a shifting of priorities by male vocalists with acting experience to emphasize emotional genuineness over pitch accuracy or voice quality in their singing performances.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".