Phonetic cue weighting in perception and production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Speech sound contrasts differ along multiple phonetic dimensions. During speech perception, listeners must decide which cues are relevant, and determine the relative importance of each cue, while also integrating other, signal-external cues. The comparison of cue weighting in perception and production bears on a range of theoretical issues including the processes underlying sound change, the time course of learning, the nature of cues, and the perception-production interface. Research examining the relative alignment of cue weighting across the modalities, on both a community and individual level, has revealed both parallels and asymmetries between the modalities. The extraordinarily wide range of ways that have been used to conceptualize and quantify cue weights reflects the inherent theoretical, methodological, and analytical differences between the two modalities. More consideration of the choices of analytical metrics, explicit discussion of the theoretical assumptions that underlie them, and systematic investigations of different types of cues will lead to more generalizable findings that can be incorporated into computational implementable models of speech processing. This article is categorized under: Linguistics > Language in Mind and Brain Psychology > Language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 it