Bibliographic record
Abstract
Masking noise is an important tool in speech and hearing experiments. It provides a means to simulate real world conditions and provide misperceptions that are as naturally elicited as possible. However, the exact characteristics of the variable approach to this parameter is poorly understood. The present investigation explores the types and variability of errors across different noise conditions. The English Consistent Confusion Corpus [Marxer et al., JASA 140, EL458–EL463 (2016)], which provides data on consistently reproducible mispercepts, was used for this investigation. The dataset contains tokens of words in multiple masking conditions: speech shaped noise; three talker babble modulated noise; and four-talker natural babble, correlated with signal to noise ratios that bias a listener towards a specific mispercept. This dataset contains 3200 individual word tokens from four different speakers with 15 listeners. The interaction between types and variability of misperception with the different types of masking noise is explored. As well, the suitability of the different noise conditions for speech perception experiments is discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".