The Effects of Digital Signal Processing Features on Children's Speech Recognition and Loudness Perception
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the effects of hearing instruments set to Desired Sensation Level version 5 (DSL v5) hearing instrument prescription algorithm targets and equipped with directional microphones and digital noise reduction (DNR) on children's sentence recognition in noise performance and loudness perception in a classroom environment. METHOD: Ten children (ages 8-17 years) with stable, congenital sensorineural hearing losses participated in the study. Participants were fitted bilaterally with behind-the-ear hearing instruments set to DSL v5 prescriptive targets. Sentence recognition in noise was evaluated using the Bamford-Kowal-Bench Speech in Noise Test (Niquette et al., 2003). Loudness perception was evaluated using a modified version of the Contour Test of Loudness Perception (Cox, Alexander, Taylor, & Gray, 1997). RESULTS: Children's sentence recognition in noise performance was significantly better when using directional microphones alone or in combination with DNR than when using omnidirectional microphones alone or in combination with DNR. Children's loudness ratings for sounds above 72 dB SPL were lowest when fitted with the DSL v5 Noise prescription combined with directional microphones. DNR use showed no effect on loudness ratings. CONCLUSION: Use of the DSL v5 Noise prescription with a directional microphone improved sentence recognition in noise performance and reduced loudness perception ratings for loud sounds relative to a typical clinical reference fitting with the DSL v5 Quiet prescription with no digital signal processing features enabled. Potential clinical strategies are discussed.
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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 | 0.004 |
| 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) | 0.002 | 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".