Automatic Documentation of Faetar’s [i]: A Methodology for Discovering Vowel Space Using Artificial Neural Networks
Bibliographic record
Abstract
Consider a huge, untagged speech corpus from a language without a written tradition. How can we quickly and accurately measure vowel space, without expending large amounts of labour and funds? We present a methodology that can be used to measure probabilistic variation across large corpora of natural spoken languages, particularly useful for under-resourced and lesser-documented languages. Using a heuristic function, the optimal vowel sample for any given phone category can be found. This heuristic is trained through machine learning, in this case, an unsupervised neural network. This process allows us to test large amounts of raw data, and create a vowel space, without the need to hand-tag many hours of recordings. We aim to model how speakers from different dialect groups speak—what are the phonetic patterns they are most likely to show, and can we differentiate and categorize unknown samples using these models created from natural language? This work uses spontaneous speech data in the endangered language Faetar, from the Heritage Language Variation and Change Corpus.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".