Hyper-articulation in Lombard speech: An active communicative strategy to enhance visible speech cues?
Bibliographic record
Abstract
This study investigates the hypothesis that speakers make active use of the visual modality in production to improve their speech intelligibility in noisy conditions. Six native speakers of Canadian French produced speech in quiet conditions and in 85 dB of babble noise, in three situations: interacting face-to-face with the experimenter (AV), using the auditory modality only (AO), or reading aloud (NI, no interaction). The audio signal was recorded with the three-dimensional movements of their lips and tongue, using electromagnetic articulography. All the speakers reacted similarly to the presence vs absence of communicative interaction, showing significant speech modifications with noise exposure in both interactive and non-interactive conditions, not only for parameters directly related to voice intensity or for lip movements (very visible) but also for tongue movements (less visible); greater adaptation was observed in interactive conditions, though. However, speakers reacted differently to the availability or unavailability of visual information: only four speakers enhanced their visible articulatory movements more in the AV condition. These results support the idea that the Lombard effect is at least partly a listener-oriented adaptation. However, to clarify their speech in noisy conditions, only some speakers appear to make active use of the visual modality.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".