Evaluating automatic syllabification algorithms for English
Bibliographic record
Abstract
Automatic syllabification of words is challenging, not least because the syllable is difficult to define precisely.This task is important for word modelling in the composition process of concatenative synthesis as well as in automatic speech recognition.There are two broad approaches to perform automatic syllabification: rule-based and data-driven.The rule-based method effectively embodies some theoretical position regarding the syllable, whereas the data-driven paradigm infers 'new' syllabifications from examples assumed to be correctly-syllabified already.This paper compares the performance of the two basic approaches.However, it is difficult to determine a correct syllabification in all cases and so to establish the quality of the 'gold standard' corpus used either to quantitatively evaluate the output of an automatic algorithm or as the example-set on which data-driven methods crucially depend.Thus, three lexical databases of pre-syllabified words were used.Two of these lexicons hold the same 18,016 words with their corresponding syllabifications coming from independent sources, whereas the third corresponds to the 13,594 words that share the same syllabifications according to these two sources.As well as one rule-based approach (Fisher's implementation of Kahn's syllabification theory), three data-driven techniques are evaluated: a look-up procedure, an exemplar-based generalization technique, and syllabification by analogy (SbA).The results on the three databases show consistent and robust patterns: the datadriven techniques outperform the rule-based system in word and juncture accuracies by a very significant margin and best results are obtained with SbA.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".