Prosodic cues to syntatic structures in speech production
Bibliographic record
Abstract
Children’s initial syntactic acquisition tasks include finding clausal and phrasal units from continuous speech stream and assigning words to grammatical categories. This study inquires if prosodic cues exist in adult speech and mark syntactic units. Participants were Quebec-French speakers. In Experiment 1 participants read Determiner+Noun and Pronoun+Verb utterances. Determiners and pronouns were French words. Nouns and verbs were pseudo-words (e.g., mige, krale, vare) counterbalanced in their occurrences in the utterances. Their prosodic properties (duration, pitch, intensity) were measured. Results showed that the two categories did not differ in prosody: noun versus verb productions of these pseudo-words were equivalent. Experiment 2 tested whether larger utterances were produced with prosodic cues supporting different grammatical categories and phrasal groupings. The same pseudo-words were the final words (counterbalanced) in 1) [Determiner+Adjective+Noun] and 2)[[Determiner+Noun]+[Verb]] structures. The second word in both structures was felli. Results showed that the last word as nouns versus verbs differed significantly in duration, pitch and intensity. Moreover, the initial consonant of verb productions was longer, with a distinct preceding pause. The second word in (2) exhibited categorical and boundary cues, differing from the second word in (1) in duration, pitch and intensity. We suggest that these acoustic cues may help infants first parse larger utterances and then acquire the syntactic properties of phrases and words based on their distribution. DOI: 10.17074/2238-975X.2015v11n1p85
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".