Computer-Assisted Voice Analysis
Bibliographic record
Abstract
OBJECTIVES: To establish and characterize the first pediatric normative database for the Multi-Dimensional Voice Program, a computerized voice analysis system, and to compare the normative data with the vocal profiles of patients with vocal fold nodules. DESIGN: A cross-sectional, observational design was used to establish the normative database. The comparative study was completed using a case-control design. SETTING: University-based outpatient pediatric otolaryngology clinic. PARTICIPANTS: One hundred control subjects (50 boys and 50 girls) aged 4 to 18 years contributed to the normative database. The voices of 26 patients (19 boys and 7 girls) with bilateral vocal fold nodules were also analyzed. MAIN OUTCOME MEASURES: Demographic data, including sex, age, height, weight, body mass index, and cigarette smoke exposure, were obtained. The Multi-Dimensional Voice Program extracted up to 33 acoustic variables from each voice analysis. RESULTS: The mean (SEM) values of each of the acoustic variables are presented. At age 12 years, boys experience a dramatic decrease in fundamental frequency measurements. The voices of patients with vocal fold nodules had significantly elevated frequency perturbation measurements compared with control subjects (P<.001). CONCLUSIONS: The vocal profile of children is uniform across all girls and prepubescent boys. Patients with vocal fold nodules demonstrated a consistent acoustic profile characterized by an elevation in frequency perturbation measurements. Normal acoustic reference ranges may be used to detect various vocal fold pathologic abnormalities and to monitor the effects of voice therapy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".