Bibliographic record
Abstract
An experiment was designed to address the importance of gross versus detailed spectral features in the perception of stop-consonant place of articulation. This is done by presenting listeners with noise-vocoded stimuli in which the spectral resolution of the processed tokens is severely reduced. While it can be assumed that such a transformation will effectively eliminate detailed spectral features, such as burst-peak and formant frequencies, previous work has shown that this is not necessarily the case [M. Kiefte, J. Acoust. Soc. Am. 106, 2273 (1999)]. Nevertheless, it is shown that gross spectral features, such as spectral tilt and compactness, are better able to predict listeners’ identifications of noise-vocoded stimuli. While this suggests that listeners attend primarily to gross spectral shape cues, which are assumed to be particularly robust in such distorted speech, it is also shown that detailed spectral features are better able to model listeners’ responses to undistorted speech. [Work supported by SSHRC.] a)Now at the Dept. of Psychology, University of Wisconsin, Madison, WI.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".