Voice quality differences in Dunan: Links between gemination and fortis-lenis contrasts
Bibliographic record
Abstract
The fortis-lenis contrast is traditionally defined by “articulatory force” and correlates with a host of measures, including closure duration, f0, amplitude, VOT, open quotient, and spectral tilt (Kohler & Dommelen, 1987; Cho et al., 2002). These measures are also features of voice quality (Keating et al., 2011; Brunelle et al., 2020) and gemination contrasts (Idemaru & Guion, 2008; Kraehenmann, 2011), and gemination has been suggested to be a type of fortis-lenis distinction (Ladd & Schmid, 2018). The current study explores acoustic correlates of initial /T K/ vs. /t k/, a contrast variably termed as initial gemination or fortis/lenis, in Dunan, a highly endangered Southern Ryukyuan language (Bentley, 2008; Yamada et al., 2015). Audio from fieldwork sessions with an elderly speaker were aligned with the Montreal Forced Aligner (McAuliffe et al., 2017) and measures of /T K t k/ and the following vowel were taken. The contrast is cued not only by closure duration and VOT, but also by f0, amplitude, H1*-H2*, H1*-A3*, and CPP differences that persist over the following vowel. This naturalistic data contributes to evidence for a constellation of acoustic features shared between fortis-lenis, gemination, and voice quality contrasts that may point to commonalities in laryngeal configuration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".