Lexically dependent estimation of acoustic information in speech
Bibliographic record
Abstract
Current estimates of the relevant acoustic information in speech are typically inventory-based. They assume a set of phonemes or featural/gestural decompositions of phonemes exists in a language, and proceed to identify and rank/weight the set of acoustic parameters sufficient to distinguish a phonemic contrast based on data from instantiations of the sounds/categories comprising those contrasts in either a controlled set of real words in the language or in nonword syllables. Acknowledging that such contrasts ultimately derive from lexical oppositions, we present an alternative, complementary approach to the quantification of acoustic information in speech: one that is lexically based. Using acoustic data from the Massive Auditory Lexical Decision project (Tucker et al., 2018)—over 26,000 unique words produced by a single speaker of Western Canadian English—and listener identification data on a representative 240-word sample of that lexicon, we define a weighted network of the phonological lexicon (cf. Vitevitch, 2008), where the weights on links between minimal pairs correspond to the predicted acoustic similarity from a model fit to the listener error distributions. From this network the distributed, “global” information contributed by individual parameters operating in an ensemble of lexical oppositions can be estimated from changes in network entropy under perturbations of those parameters.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".