Opposing forces on acoustic duration
Bibliographic record
Abstract
The present paper investigates the influence of opposing lexical forces on speech production using the duration of the stem vowel of regular and irregular verbs as attested in the Buckeye corpus of conversational North-American English. We compared two sets of predictors, reflecting two different approaches to speechproduction, one based on competition between word forms, the other based on principles of discrimination learning. Classical measures in word form competition theories such as word frequency, lexical density, and gang size (types of vocalic alternation) were predictive of stem vowel duration. However, more precise predic-tions were obtained using measures derived from a two-layer network model trained on the Buckeye corpus. Measures representing strong bottom-up support predicted longer vowel durations. Conversely, measures reflecting uncertainty predicted shorter vowel durations, including a measure of the verb’s semantic density. The learning-based model also suggests that it is not a verb’s frequency as such that gives rise to shorter vowel duration, but rather a verb’s collocational diversity. Results are discussed with reference to the Smooth Signal Redundancy Hypothesis and the Paradigmatic Signal Enhancement Hypothesis.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".