Automatic Documentation of Faetar’s [i]: A Methodology for Discovering Vowel Space Using Artificial Neural Networks
Why this work is in the frame
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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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it