Effects of Low-Pass Filtering on the Perception of Word-Final Plurality Markers in Children and Adults With Normal Hearing
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the effect of low-pass filtering on the detection of word-final /s/ and /z/ for children and adults with normal hearing. METHOD: Stimuli were nouns from the University of Western Ontario Plurals Test (Glista & Scollie, 2012), low-pass filtered with 5 different cutoff frequencies: 8000 Hz, 5000 Hz, 4000 Hz, 3000 Hz, and 2000 Hz. Listeners were children (age range = 7-13 years) and adults with normal hearing. The task was a 2-alternative forced-choice task with a picture-pointing response. RESULTS: Performance was worse for lower than for higher low-pass filter cutoff frequencies, but the effect of low-pass filtering was similar for children and adults. Nearly all listeners achieved 100% correct performance when stimuli were low-pass filtered with cutoff frequencies of 8000 Hz or 5000 Hz. Performance remained well above chance even for the most severe filtering condition (2000 Hz). Restricting high-frequency audibility influenced performance for plural items to a greater extent than for singular items. CONCLUSION: The results indicate that children and adults with normal hearing can use acoustic information below the spectral range of frication noise typically associated with /s/ and /z/ to discriminate between singular and plural forms of nouns in the context of the University of Western Ontario Plurals Test.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".