The development of the Language-Independent Speech in Noise and Reverberation test (LISiNaR) and evaluation in listeners with English as a second language
Bibliographic record
Abstract
OBJECTIVE: Create a language-independent, ecologically valid auditory processing assessment and evaluate relative stimuli intelligibility in native and non-native English speakers. DESIGN: The Language-Independent Speech in Noise and Reverberation Test (LISiNaR) targets comprised consonant-vowel (CVCV) pseudo-words. Distractors comprised CVCVCVCV pseudo-words. Stimuli were presented over headphones using an iPad either face-to-face or remotely. Scoring occurred adaptively to establish a participant's speech reception threshold in noise (SRT). The listening environment was simulated using reverberant and anechoic head-related transfer functions. In four test conditions, targets originated from 0°. Distractors originated from either ±90°, ±67.5° and ±45° (spatially separated) or 0° azimuth (co-located). Reverberation impact (RI) was calculated as the difference in SRTs between the anechoic and reverberant conditions and spatial advantage (SA) as the difference between the spatially separated and co-located conditions. STUDY SAMPLE: = 24) and Canadian (25) and non-native English speakers (34). RESULTS: No significant effects of language occurred for the test conditions, RI or SA. A small but significant effect of delivery mode occurred for RI. Reverberation impacted SRT by 5 dB relative to anechoic conditions. CONCLUSION: Performance on LISiNaR is not affected by the native language or accent of groups tested in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".