Measurement and prediction of speech and noise levels and the Lombard effect in eating establishments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Measurements made of the acoustical characteristics of, and occupied noise levels in, ten eating establishments are described. Levels to which diners and employees were exposed varied from 45 to 82 dB(A). From these levels and diner questionnaire responses, the number of customers present and average noise levels to which individual diners were exposed during their visits were estimated. These data, assumptions about the number of talkers per customer, and classical room-acoustical theory were used to deduce talker voice output levels. These varied from slightly above "casual" to "loud." An iterative model for predicting speech and noise levels in eating establishments, including the Lombard effect as described by a new, proposed model, was developed. With the measured noise levels as the target for prediction, optimization techniques were used to find best estimates of unknown prediction parameters--such as those defining the Lombard effect, the number of talkers per customer, and the average absorption per customer--with highly credible results. The prediction algorithm and optimal parameters constitute a novel model for predicting speech and noise levels--and thus speech intelligibility--in eating establishments, as a function of the number of customers, including a proven, realistic model of the Lombard effect.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it