Bibliographic record
Abstract
Articulatory learning models utilize traditional neural network approaches to model speech. However,they differ in their inclusion of an intermediary step in which the model is trained to produce sound utilizing a physical model of the human vocal tract. This style of training is theoretically able to represent the impact of the physical nature of the human vocal tract on language in a way that is more directly translatable to the physical world. This project seeks to create an articulatory learning model which is trained to recognize and repeat input sounds utilizing the University of Wisconsin’s X-Ray Microbeam Database. We attempt this utilizing an inverse model which seeks to predict the vocal tract configuration of a given speech signal, and a forward model which predicts the acoustic form produced by vocal tract configurations. Further, we analyze the performance of the model across various training lengths to investigate the comparison to child language learning that is commonly made when implementing models of this type. Should this analysis of articulatory learners be valid, we look to find commonalities in the phonetic patterning of articulatory learning neural networks and those observed in the phonetic acquisition stages of child language development.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".