Finding Phrases: The Interplay of Word Frequency, Phrasal Prosody and Co-speech Visual Information in Chunking Speech by Monolingual and Bilingual Adults
Bibliographic record
Abstract
The audiovisual speech signal contains multimodal information to phrase boundaries. In three artificial language learning studies with 12 groups of adult participants we investigated whether English monolinguals and bilingual speakers of English and a language with opposite basic word order (i.e., in which objects precede verbs) can use word frequency, phrasal prosody and co-speech (facial) visual information, namely head nods, to parse unknown languages into phrase-like units. We showed that monolinguals and bilinguals used the auditory and visual sources of information to chunk "phrases" from the input. These results suggest that speech segmentation is a bimodal process, though the influence of co-speech facial gestures is rather limited and linked to the presence of auditory prosody. Importantly, a pragmatic factor, namely the language of the context, seems to determine the bilinguals' segmentation, overriding the auditory and visual cues and revealing a factor that begs further exploration.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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 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".