Bibliographic record
Abstract
The notion of acoustic distance figures into many aspects of phonetics, including phonological neighborhoods. A measurement of word-level acoustic distance useful for cognitive modeling must account for two listener characteristics: sensitivity to acoustic differences and sensitivity to duration discrepancies between words. The present work used dynamic time warping to measure how acoustic distance accumulates between words over time. The results of a distance rating task with synthesized vowels were used as a basis for selecting a mathematical function that best matched listener sensitivities. Additionally, the results of a reminder task with synthesized vowels were used to determine a just noticeable difference threshold for vowel duration. The results suggested that a distance function based on the 4.5-norm using a 30 ms radius for dynamic time warping best matched human behavior. A third analysis used these dynamic time warping configurations to model reaction times in an auditory lexical decision task and found that Euclidean distance and no temporal constraints on dynamic time warping best matched human behavior. These results are discussed in relation to spoken word recognition models, including how to assess the acoustic match between the speech signal and a word in the lexicon.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".