Interactive Effects of Low–Pass Filtering and Masking Noise on Word Recognition
Bibliographic record
Abstract
A word recognition in noise paradigm was employed to examine temporal resolution in individuals with simulated hearing loss. Word recognition scores were obtained for low-pass filtered speech (i.e., cutoff frequencies of 1000,1250, and 1500 Hz) presented in continuous and interrupted noise at signal-to-noise ratios (SNRs) of -10, 0, and 10 dB. Performance improved with increasing SNR and low-pass frequency filter settings. Generally, word recognition performance was better in the interrupted noise condition than the continuous noise condition. This effect was greatest in the -10 dB SNR condition. Since the continuous/interrupted performance difference steadily declined as a function of low-pass filter cutoff frequency, these findings suggest that one factor leading to poorer speech recognition in individuals with high-frequency hearing impairment may be their dependence on low-frequency hearing channels that are inherently poorer than high-frequency channels for temporal resolution.
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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) | 0.002 | 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".