Reverberation time and maximum background-noise level for classrooms from a comparative study of speech intelligibility metrics
Bibliographic record
Abstract
Speech intelligibility metrics that take into account sound reflections in the room and the background noise have been compared, assuming diffuse sound field. Under this assumption, sound decays exponentially with a decay constant inversely proportional to reverberation time. Analytical formulas were obtained for each speech intelligibility metric providing a common basis for comparison. These formulas were applied to three sizes of rectangular classrooms. The sound source was the human voice without amplification, and background noise was taken into account by a noise-to-signal ratio. Correlations between the metrics and speech intelligibility are presented and applied to the classrooms under study. Relationships between some speech intelligibility metrics were also established. For each noise-to-signal ratio, the value of each speech intelligibility metric is maximized for a specific reverberation time. For quiet classrooms, the reverberation time that maximizes these speech intelligibility metrics is between 0.1 and 0.3 s. Speech intelligibility of 100% is possible with reverberation times up to 0.4-0.5 s and this is the recommended range. The study suggests "ideal" and "acceptable" maximum background-noise level for classrooms of 25 and 20 dB, respectively, below the voice level at 1 m in front of the talker.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".