Acoustical impacts of blindness on speech production strategies linked to Lombard speech and audiovisual interaction
Bibliographic record
Abstract
This paper investigates the acoustical correlates of Lombard speech and audiovisual interaction, in blind and sighted adults. Ten blind and nine sighted adults were recorded while producing repetitions of the French vowels /i/, /u/, /a/ in a “pVp” context. Sentences were produced under four conditions, varying in terms of interaction type (whether the speaker could be heard and seen—audiovisual interaction—or only heard— auditory interaction—by the interlocutor) and presence/absence of noise. Inter-vocalic and intra-vocalic formant dispersion as well as fundamental frequency (F0), intensity and vowel duration values were measured. Results of linear mixed effects models showed that blind speakers increased their inter-vocalic distances in noise, but decreased it when they were told they were seen by the interlocutor (audiovisual interaction). Blind speakers decreased F0 in the audiovisual conditions while sighted speakers increased F0 in noise. Finally, sighted speakers produced louder vowels in the noisy conditions and in the auditory interactive conditions. This pattern was only found for unrounded vowels in blind speakers. These results highlight the role of vision on speech production and show that sighted speakers likely use active strategies to enhance visual cues in audiovisual interactions, compared to auditory only interactions.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".