Following the Voices of our Ancestors: Studying the pronunciation of Wendat language learners
Bibliographic record
Abstract
As Wendat, I dream that future Wendat will learn our language without a French or English accent. Accent is most easily and effectively acquired through consistently mimicking native speakers. This proves difficult in the case of Wendat, as we are revitalising a sleeping language, and so even the best speakers inevitably have an accent currently (generally a Quebecois accent). This research begins exploring the efficacy of mimicking audio that has been edited to approximate un-accented Wendat using recordings of ancestral Wyandot. In order to do this, a phonetic analysis of the Wyandot corpus was first completed for the stops /t/ and /k/ as produced by Sarah Dushane in 1967, novel utterances were edited to match, and lastly language learner's pre- & post-mimicking productions were recording and analysed. Overall, preliminary findings indicate the process to be effective. With further research into the rest of the language's phonetic inventory and improved efficacy of the audio editing process, this technique could ensure that the Wendat spoken by our descendants is as free of colonial impacts as possible.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".