Acoustic correlates of information structure
Bibliographic record
Abstract
This paper reports three studies aimed at addressing three questions about the acoustic correlates of information structure in English: (1) do speakers mark information structure prosodically, and, to the extent they do; (2) what are the acoustic features associated with different aspects of information structure; and (3) how well can listeners retrieve this information from the signal? The information structure of subject–verb–object sentences was manipulated via the questions preceding those sentences: elements in the target sentences were either focused (i.e., the answer to a wh-question) or given (i.e., mentioned in prior discourse); furthermore, focused elements had either an implicit or an explicit contrast set in the discourse; finally, either only the object was focused (narrow object focus) or the entire event was focused (wide focus). The results across all three experiments demonstrated that people reliably mark (1) focus location (subject, verb, or object) using greater intensity, longer duration, and higher mean and maximum F0, and (2) focus breadth, such that narrow object focus is marked with greater intensity, longer duration, and higher mean and maximum F0 on the object than wide focus. Furthermore, when participants are made aware of prosodic ambiguity present across different information structures, they reliably mark focus type, so that contrastively focused elements are produced with greater intensity, longer duration, and lower mean and maximum F0 than noncontrastively focused elements. In addition to having important theoretical consequences for accounts of semantics and prosody, these experiments demonstrate that linear residualisation successfully removes individual differences in people's productions thereby revealing cross-speaker generalisations. Furthermore, discriminant modelling allows us to objectively determine the acoustic features that underlie meaning differences.
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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 | 0.008 |
| 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 | 0.000 | 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".