Reduction of hypernasal speakers’ nasalance scores with voice focus adjustments: Replication and expansion of findings
Bibliographic record
Abstract
Speech therapy exercises are not considered effective to reduce hypernasality in the speech of children with cleft palate. Previous research studies have shown that nasalance scores of hypernasal speakers were lower in backward and higher in forward voice focus. Conversely, some individual speakers had lower nasalance scores in forward voice focus. The present study sought to replicate and further expand these findings. The study investigated how many hypernasal speakers in a small convenience sample could lower their nasalance using voice focus. For 6 hypernasal speakers (4 F, 2 M, ages 5-18) with repaired cleft palate, nasalance scores were recorded for a non-nasal sentence, a nasal sentence, 2 phonetically varied sentences and a short song at baseline, in backward voice focus, in forward voice focus and at a final baseline. For individual speakers, reductions in nasalance of -10% from baseline were considered meaningful. Mean nasalance scores for all stimuli combined changed significantly from 58.2% nasalance (SD 11.3) at baseline to 44.2% (SD 15.3) in backward voice focus, 60.8% (SD 17.6) in forward voice focus, and 53.0% (SD 11.9) at the final baseline. Nasalance scores in the backward voice focus were significantly lower than the initial baseline and forward voice focus conditions. Inspection of individual scores showed that 4 of the 6 participants showed pronounced reductions in nasalance scores of up to -44% in backward and up to -23% in forward voice focus. Further research about the potential effectiveness of this approach for speech therapy should be undertaken.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".