The phonetic motivation of stop assibilation
Bibliographic record
Abstract
The present study is concerned with stop assibilation — a process whereby stops become sibilant affricates or sibilant fricatives before high vocoids. Two examples are presented in (1a, b) from Finnish and Korean respectively. Similar examples of assibilations can be found in Romanian, Cheyene, Efik, Japanese and Quebec French (see Bhat 1978 and Kim 2001). (1) a. t → s / _ _ i b. t t → ts ts / _ _ i Stop assibilations are defined here as processes with the following four properties (see also Clements 1999 and Kim 2001): (a) the input segments are stops, which are usually alveolar or dental, (b) the trigger is (typically) some subset of the high front vocoids (e.g. /i j/), (c) the output is always a sibilant (either an affricate or a fricative) and (d) the trigger is always to the right of the target. Kim (2001) offers a phonetic explanation for these properties: The creation of sibilants from stops has its phonetic origin in the brief period of turbulence which occurs at the release of a stop into a high vocoid. In the present study we present phonetic evidence supporting the two implications in (2), neither of which is discussed by Clements (1999) or Kim (2001): (2) a. Assibilation of /t / in /tj / implies assibilation of /t / in /ti/ b. Assibilation of /d / implies the assibilation of /t/ Both (2a) and (2b) can be confirmed by examining the cross-linguistic evidence for stop
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".