Where does the gamma go?: Acoustics of the non-labialized voiced velar approximant in Tlingit
Bibliographic record
Abstract
The acoustic properties of the voiced velar approximant [ɰ] are poorly characterized, partly from understudy of relevant languages and partly from uncertainty about the sound itself. We investigate this sound in Tlingit, a critically endangered indigenous language of northwestern North America, where [ɰ] contrasts with labialized [w] and palatal [j] in some dialects but not in others where it was lost through sound change (reductive primary split with merge). We use audio recordings of spoken narratives spanning much of the 20th century from speakers with and without [ɰ] to characterize this sound both dialectally and diachronically. We measure formants (f1–f3) to identify place information, amplitude for oral aperture, and harmonic-to-noise ratio for manner. Existing descriptions and phonological patterns predict that [ɰ] is an approximant and not a fricative so we compare against known approximants for differences in turbulence and spectral properties. We also compare against labialized sounds including contrastive [w] for absence of lowered f3 to exclude lip rounding in [ɰ]. We argue that it is the intersection of these acoustic cues that distinguishes [ɰ] from similar sounds, but that these cues are unstable so that dialectal sound change arises from misperception in perturbations of their production.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".