Voice Onset Time in a language without voicing contrast: An acoustic analysis of Blackfoot oral stops
Bibliographic record
Abstract
This paper presents an acoustic analysis of Voice Onset Time (VOT) in oral stop consonants in Blackfoot, an Algonquian language without contrastive voicing. We focus on VOT as one of the key temporal acoustic correlates of voicing and investigate VOT variation in relation to (i) place of articulation (labial vs. alveolar vs. velar); (ii) length (long vs. short), quality (/a/ vs. /o/), and accent pattern (accented vs. unaccented) of the following vowel; (iii) word position (initial vs. medial); (iv) gender; and (v) age. We analyzed 2096 stop consonant tokens produced by 13 participants, who completed two different tasks: an English-to-Blackfoot translation task and a picture naming task. The key findings are as follows: (i) Blackfoot stop consonants fall into the short-lag range that overlaps with the English voiced category, with mean VOT values ranging from 11.6–32.7 ms; (ii) VOT values become progressively longer as the place of articulation moves to more posterior positions; (iii) VOT values are longer before high vowels than before low vowels; (iv) VOT values are longer before long vowels than before short vowels; (v) no statistically significant effect was found for the linguistic factors word position and accent pattern; (vi) no statistically significant effect was found for the socio-indexical factors age and gender; and (vii) no statistically significant effect was found for the experimental factor task type. The implications of our findings for the Blackfoot writing system and ongoing work on language documentation and revitalization are briefly discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".