Listening to speech in noise : Evaluation and training
Bibliographic record
Abstract
Objective : Examine the contribution of speech tests when measuring the benefits of a speech listening training program in noise. Methods : Children with auditory processing disorders and adults reporting speech listening difficulties in noise were trained to listen to speech stimuli presented in a noisy background during two 30-minute sesssions per week, for nine to thirteen weeks. Pre and post-training measures were done, in part, with speech tests in noise: the French adaptation of the Hearing In Noise Test (Vaillancourt et al., 2008), the Sentence in noise test (Test de phrases dans le bruit – TPB – Lagace et al., 2010) or the Word in noise test (Test de mots dans le bruit – TMB – Lagace, 2010). Results/Conclusion : Results suggest (1) greater tolerance to noise when listening to speech stimuli in noise across the training sessions and (2) that speech tests could be sensitive to changes following the training program in some, but not all individuals who participated into the program. References Lagace, J. (2010). Developpement du test de mots dans le bruit: mesure de l'equivalence des listes et donnees preliminaires sur l'effet d'âge. Canadian Acoustics, 38 , 19-30. Lagace, J., Jutras, B., Giguere, C., & Gagne, J.-P. (2010). Development of the Test de Phrases dans le Bruit (TPB) Elaboration du Test de phrases dans le bruit (TPB). Revue canadienne d’orthophonie et d’audiologie, 34 , 261-270. Vaillancourt, V., Laroche, C., Giguere, C. & Soli S.D. (2008). Establishement of age-specific normative data for the canadian French version of the hearing in noise test for children. Ear & Hearing, 29 , 453-466.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".