Prediction of speech intelligibility in rooms—a comparison of five methods
Bibliographic record
Abstract
In this paper, five methods for calculating speech intelligibility at listener positions in rooms, from measured impulse responses and speech and noise levels, are presented and compared. All methods involve combining a measure of room reverberation with the received signal-to-noise level differences in the occupied room. The first method involves measured octave-band C50s and signal-to-noise level differences to determine octave-band U50s. The remaining four methods comprise detailed (involving octave-band values) and simplified (involving single- and/or combined-frequency values) versions of two approaches. The first approach (second and third methods) involves measured background-noise levels and values of the Transmission Index measured for infinite signal-to-noise level difference. The second approach (fourth and fifth methods) involves 1000-Hz octave-band early-decay times and A-weighted signal-to-noise level differences. The five methods are briefly presented. Then, predictions by the methods for a number of classrooms are presented and compared, and the differences, the merits, and the disadvantages of the various methods are discussed.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".