Using Discrete Cosine Transformations to Characterize Tones in Two Athabaskan Languages
Bibliographic record
Abstract
Tone in North American languages has received very little phonetic attention. This paper is a preliminary analysis of the production of lexical tone in two related bitonal (H/L) Athabaskan languages spoken in Northwestern Canada: Dene Suline (Chipewayan) and Tlicho Yatii (Dogrib). These languages have opposing polar-type systems. In this study we look at 2-syllable words taken from existing recordings of word lists by native fluent speakers in and Tlicho Yatii (‘H marked’) and Dene Suline (‘L marked’). There were 3 speakers for each language. We use the discrete cosine transformation (DCT) to characterize the tone trajectories of these two languages. The DCT is a transformation that decomposes a spline into a set of coefficients (k0-kn) from which the spline can be reconstructed. K0 is proportional to mean f0; k1 to slope and k2 to (parabolic) curve. The DCT coefficients are all real numbers which allows for a simple numerical correlate for trajectory shape. The findings suggest that the realization of the contrastive tones is quite similar between the two languages. However, there were differences in the realization of H tones in stems in Tlicho Yatii that may reflect an interaction of tone and vowel length. Also, while both languages make a f0 distinction as would be expected in a H/L tonal system, in Dene Suline H tone tends to resist a fall in the stem, unlike L tones, and the tones in Tlicho Yatii This study implicates the importance of the differences in tonal specifications and alignment among the Dene tone languages in understanding tone patterns and tonogenesis. Future studies involve the interaction of tone, especially H tone and tonal alignment with vowel length, and the distinction between the realization of tone in the stem versus pre-stem domain.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".