The impact of dialect on the ability to understand speech-in-noise
Bibliographic record
Abstract
Difficulty understanding speech-in-noise (SIN) is one of the most commonly reported hearing issues for older adults. Thus, being able to accurately assess an individuals’ ability to understand SIN is of utmost importance. A number of standardized assessments have been developed to quantify this ability. These tests normally use pre-recorded speech as the target stimulus, and thus the language and dialect of each test cannot be easily modified. One issue that has received scant attention is how dialect impacts performance on a standardized SIN test. There is some evidence that it is more difficult to understand SIN in your native language, but not your native dialect. How this difficulty translates to a standardized, clinical SIN assessment is poorly understood. To address this issue, the QuickSIN was administered to a sample of native speakers of Newfoundland English. The QuickSIN is a standardized SIN assessment, and the target sentences are spoken in an English dialect that comes from the northern United States. The participants from Newfoundland performed outside the 95% CIs for the QuickSIN, despite having normal audiometric thresholds, which suggests that difficulties with dialect and not with hearing can contribute poorer performance on a SIN test in a clinical setting. The negative effect of dialect mismatch on clinical SIN assessments limits the ability for clinicians to accurately quantify SIN abilities in people whose native dialect does not match the test dialect.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".