The Relationship Between Pitch Discrimination and Acoustic Voice Measures in a Cohort of Female Speakers
Bibliographic record
Abstract
BACKGROUND: Evidence across a range of musically trained, hearing disordered and voice disordered populations present conflicting results regarding the relationship between pitch discrimination (PD) and voice quality. PD characteristics of female speakers with and without a musical training background and no self-reported voice disorder, and the relationship between PD and voice quality in this particular population, have not been investigated. AIMS: To evaluate PD characteristics in a cohort of female participants without a self-reported voice disorder and the relationship between PD and acoustic voice measures. METHOD: One hundred fourteen female participants were studied, all of whom self-reported as being non-voice disordered. All completed the Newcastle Assessment of Pitch Discrimination which involved a two-tone PD task. Their voices were recorded producing standardized vocal tasks. Voice samples were acoustically analyzed for frequency-domain measures (fundamental frequency and its standard deviation, and harmonics-to-noise ratio) and spectral-domain measures (cepstral peak prominence and the Cepstral/Spectral Index of Dysphonia). Data were analyzed for the whole cohort and for musical and non-musical training backgrounds. RESULTS: In the whole cohort, there were no significant correlations between PD and acoustic voice measures. PD accuracy in musically trained speakers was better than in non-trained speakers and correlated with fundamental frequency standard deviation in prolonged vowel tasks. Vocalists demonstrated superior PD accuracy and fundamental frequency standard deviation in prolonged vowels compared to instrumentalists but did not show significant correlations between PD and acoustic measures. The Newcastle Assessment of Pitch Discrimination was a reliable tool, showing moderate-good prediction value in differentiating musical background. CONCLUSIONS: There was little evidence of a relationship between PD and acoustic measures of voice quality, regardless of musical training background and superior PD accuracy among the musically trained. These data do not support ideas concerning the co-development of perception and action among individuals identified as having voice quality measures within normal ranges. Numerous measures of voice quality, including measures sensitive to pitch, did not distinguish across musically and non-musically trained individuals, despite individual differences in pitch discrimination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".