A new algorithm for the alignment of phonetic sequences
Why this work is in the frame
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Bibliographic record
Abstract
Alignment of phonetic sequences is a necessary step in many applications in computational phonology. After discussing various approaches to phonetic alignment, I present a new algorithm that combines a number of techniques developed for sequence comparison with a scoring scheme for computing phonetic similarity on the basis of multivalued features. The algorithm performs better on cognate alignment, in terms of accuracy and efficiency, than other algorithms reported in the literature. 1 Introduction Identification of the corresponding segments in sequences of phones is a necessary step in many applications in both diachronic and synchronic phonology. Usually we are interested in aligning sequences that represent forms that are related in some way: a pair of cognates, or the underlying and the surface forms of a word, or the intended and the actual pronunciations of a word. Alignment of phonetic sequences presupposes transcription of sounds into discrete phonetic segments, and so diff...
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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