Reliability and Task Effects in CAPE-V Auditory-Perceptual Voice Assessments: Insights From the PVQD30 Subset
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the inter- and intra-rater reliability of consensus auditory-perceptual evaluation of voice (CAPE-V) auditory-perceptual ratings and explore task-specific differences (sustained vowels versus sentences) in ratings and reliability. STUDY DESIGN: ). METHODS: Thirty voice samples representing varying dysphonia severities were selected from the Perceptual Voice Qualities Database. Eight Quebecois speech-language pathologists (SLPs) rated the samples using the CAPE-V protocol on the Bridge2Practice platform. Ratings included six vocal features on a visual analog scale (VAS) and binary consistency (C/I) judgments. Reliability was assessed using intra-class correlation coefficients (ICCs) for VAS ratings and Gwet's AC1 for C/I ratings. Task effects were analyzed using Wilcoxon signed-rank tests and Spearman correlations. RESULTS: Overall severity ratings demonstrated good inter-rater reliability for both vowels (ICC = 0.79) and sentences (ICC = 0.87). Pitch and loudness ratings showed low inter-rater reliability (ICCs < 0.5) across tasks. Vowels were rated as more impaired for most features, except strain, which showed higher impairment on sentences. Inter-rater reliability was higher for roughness and breathiness on vowels, whereas strain showed better reliability on sentences. Intra-rater reliability was consistently higher on sentences for all features (ICCs > 0.75 for most). Consistency ratings were more reliable on vowels than sentences for most features, except loudness. CONCLUSIONS: Task type significantly impacts CAPE-V ratings and their reliability. Vowels provided higher inter-rater reliability for roughness and breathiness, while sentences yielded better intra-rater consistency and strain reliability. These findings highlight the need for ongoing refinement of assessment tools and training protocols to ensure accurate and reliable voice evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".