Auditory and visual clear speech effects measured during a simulated conversational intercation
Bibliographic record
Abstract
Iterations of sentences were recorded audio-visually from talkers while they participated in a speech-tracking task. Six female talkers produced iterations of conversational and clear speech under two different experimental conditions: (a) while the talker was informed that only her visual–speech cues would be transmitted to the interlocutor and (b) while she was informed that only her auditory–speech cues would be transmitted to the interlocuter. In reality, both her auditory– and her visual–speech cues were recorded under each experimental condition. Target sentences were extracted for the recordings, edited, and presented in a random order to a group of 48 subjects. The subjects completed a speech-recognition task under two perceptual modalities: auditory-only and visual-only. The subjects’ mean speech-recognition scores were used to determine the speech intelligibility scores of individual talkers for each experimental condition. The results failed to reveal any differences between the speech intelligibility scores obtained while a talker intended to produce iterations of visual-clear speech and those obtained while she intended to produce iterations of auditory-clear speech. Hence, the findings failed to demonstrate that talkers modify their articulation patterns in order to compensate for the perceptual modality under which the interlocutor receives the speech information. [Work supported by a NSERC grant awarded to J-PG.]
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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 | 0.006 |
| 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) | 0.002 | 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".