Prediction of optimal conditions for verbal-communication quality in eating establishments
Bibliographic record
Abstract
This paper discusses the prediction of verbal-communication quality in eating establishments (EEs). EEs contain talkers and listeners who require high speech intelligibility at their tables, and high speech privacy between tables. Using catt-Acoustic, verbal-communication quality--quantified by speech transmission index (STI)--in models of three existing EEs was predicted. Talker voice-output levels were predicted using an existing empirical model accounting for the Lombard effect. With these, catt-Acoustic predicted impulse responses, speech levels and noise levels at primary and secondary listener positions, and the corresponding STIs. The untreated EEs were first modeled for various talker and listener positions, and occupancies. Then various treated configurations, involving reduced volume, increased absorption and barriers were studied to determine the effectiveness of the treatments. The results suggest that placing barriers around tables can be an effective way to achieve good verbal-communication quality. Increasing the absorption of the room surfaces or decreasing the ceiling height to control reverberation may not be effective. However, increasing the surface absorption and putting barriers around tables may achieve optimal speech conditions in EEs. Subdividing large EEs into smaller ones can also be effective.
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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 | 0.003 |
| 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 | 0.001 | 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".