Children’s Speech Recognition and Loudness Perception With the Desired Sensation Level v5 Quiet and Noise Prescriptions
Bibliographic record
Abstract
PURPOSE: To determine whether Desired Sensation Level (DSL) v5 Noise is a viable hearing instrument prescriptive algorithm for children, in comparison with DSL v5 Quiet. In particular, the authors compared children's performance on measures of consonant recognition in quiet, sentence recognition in noise, and loudness perception when fitted with DSL v5 Quiet and Noise. METHOD: Eleven children (ages 8 to 17 years) with stable, congenital sensorineural hearing losses participated in the study. Participants were fitted bilaterally to DSL v5 prescriptions with behind-the-ear hearing instruments. The order of prescription was counterbalanced across participants. Repeated measures analysis of variance was used to compare performance between prescriptions. RESULTS: Use of the Noise prescription resulted in a significant decrease in consonant perception in Quiet with low-level input, but no difference with average-level input. There was no significant difference in sentence-in-noise recognition between the two prescriptions. Loudness ratings for input levels above 72 dB SPL were significantly lower with the noise prescription. CONCLUSIONS: Average-level consonant recognition in quiet was preserved and aversive loudness was alleviated by the Noise prescription relative to the quiet prescription, which suggests that the DSL v5 Noise prescription may be an effective approach to managing the nonquiet listening needs of children with hearing loss.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".