Audio-visual clear speech: Articulation, acoustics and perception of segments and tones
Bibliographic record
Abstract
Research has established that clear speech with enhanced acoustic signal benefits segmental intelligibility. Less attention has been paid to visible articulatory correlates of clear-speech modifications, or to clear-speech effects at the suprasegmental level (e.g., lexical tone). Questions thus arise as to the extent to which clear-speech cues are beneficial in different input modalities and linguistic domains, and how different resources are incorporated. These questions address the fundamental argument in clear-speech research with respect to the trade-off between effects of signal-based phoneme-extrinsic modifications to strengthen overall acoustic salience versus code-based phoneme-specific modifications to maintain phonemic distinctions. In this talk, we report findings from our studies on audio-visual clear speech production and perception, including vowels and fricatives differing in auditory and visual saliency, and lexical tones believed to lack visual distinctiveness. In a 3-stream study, we use computer-vision techniques to extract visible facial cues associated with segmental and tonal productions in plain and clear speech, characterize distinctive acoustic features across speech styles, and compare audio-visual plain and clear speech perception. Findings are discussed in terms of how speakers and perceivers strike a balance between utilizing general saliency-enhancing and category-specific cues across audio-visual modalities and speech styles with the aim of improving intelligibility.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".