Cross-linguistic realization of lateral ejective affricates in connected versus isolated speech
Bibliographic record
Abstract
Typological research on ejectives has focused on the realization of stops in isolated speech (Kingston 1985, Lindau 1984). However, there has been little research on the realization of ejectives, and in particular affricates, in connected versus isolated speech. Given that lateral affricates can be produced with variable realization, this study compares the acoustics of [tɬ’] in isolated and connected speech for speakers of three languages: Lushootseed (Coast Salish), Hul’q’umi’num’ (Coast Salish), and Dene Kədə́ (Dene/Athabaskan). Duration, spectral moment, and voice quality measurements were examined from corpus data of word lists and connected speech. Results indicated that there was greater voice onset time (VOT), longer closure duration, and a smaller frication duration to VOT ratio in isolated speech than connected speech, supporting Lindblom’s (1990) and Farnetani & Recasens’ (2013) view that words are produced more hyperarticulated in isolated speech. Cross-linguistic differences were found in the duration of frication, center of gravity, and the rise to peak amplitude of the following vowel. Dene Kədə́ had greater frication duration and a shallower intensity slope than the Salish languages, indicators of differences in place of articulation and degree of affrication. This suggests different realizations of [tɬ’] across languages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".