Effect of listener training on perceptual judgement of hypernasality
Bibliographic record
Abstract
Reliable perceptual judgement is important for documenting the severity of hypernasality, but high reliability can be difficult to obtain. This study investigated the effect of practice and feedback on intra-judge and inter-judge reliability of hypernasality judgements. The judges were 36 speech-language therapy students, who were randomly assigned to three groups for training: (1) Exposure (simple exposure to hypernasal speech samples), (2) Practice-only (practice with hypernasality judgements without feedback), and (3) Practice-Feedback (practice with hypernasality judgements with feedback). After training, the judges rated hypernasality in non-nasal sentences produced by 20 speakers with hypernasality and two normal speakers, using direct magnitude estimation. Both practice groups showed fair-to-good inter-judge reliability for rating the female samples: had more listeners who showed significant intra-judge reliability, and had significantly larger range of hypernasality ratings than the exposure group. To conclude, practice (with or without feedback) is useful for improving the reliability of hypernasality ratings.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".