The relationship of VOT and F0 contrasts across speakers and words in the German voicing contrast
Bibliographic record
Abstract
Recent studies on tonogenesis in progress in Seoul Korean (Kang, 2014; Bang et al., 2015) find that the size of the VOT contrast and the f0 contrast between aspirated and lax stops “trade off” across speakers (e.g., male speakers have greater/smaller VOT/f0 contrasts), as well as words (e.g., different frequencies, following vowel heights). We examine whether this parallelism across speakers and words occurs in a language not undergoing tonogenesis by examining the size of the fortis/lenis contrast in VOT and f0 in German, using speech from the PhonDat corpus (Draxler, 1995). Mixed-effect regression models show that the size of the VOT contrast, but not the f0 contrast, is affected by properties of words (e.g., frequency, vowel height), unlike the parallelism observed in Korean. We further investigated whether VOT/f0 parallelism would be observed across speakers by partialing out linguistic factors affecting VOT/f0, and performing one logistic regression per speaker (n = 76) of fortis/lenis class as a function of residualized f0 and VOT. The cue weights of F0 and VOT are negatively correlated across speakers (r=-0.213, p = 0.0213), suggesting parallelism exists across speakers, but not across words.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".