Effects of variation on processing of word-medial consonants
Bibliographic record
Abstract
The present study investigates processing of variation in word-medial stops and the role of this variation in phoneme recognition. Variation in word-medial stops has been shown to influence lexical access but it is unclear whether this variation also affects recognition at the phoneme level [Tucker, J. Phon., 39, 312-318, (2011)]. A phoneme monitoring task is used to investigate the role of production variation in the identification of word-medial stop consonants. Following Warner & Tucker [J. Acoust. Soc. Am., 130, 1606-1617, (2012)], the stimuli comprise English nonwords with target consonants /b, p, d, t, g, k/ in an intervocalic post-stress position produced with a range of production variation. This variation was grouped into three sets including groups at each extremes and a group in the middle of the range. The acoustic characteristics of the stops, which reflect this variation (e.g. consonant duration and intensity difference) were extracted and used to model the reaction times collected during the phoneme monitoring experiment. The reaction times were statistically modeled using a linear mixed-effects regression. Models of spoken word recognition and models of lexical storage are used to interpret the results and the contribution of the results to our understanding of these models is discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".