A new system of speech privacy criteria in terms of Speech Privacy Class (SPC) values
Bibliographic record
Abstract
This paper describes a new system` of speech privacy criteria in terms of Speech Privacy Class (SPC) values. SPC values can be used to specify the required speech privacy for new construction or to assess the speech privacy of ex-isting closed rooms. The ASTM E2638 measurement standard defines SPC as the sum of the measured average noise level at the position of a potential eavesdropper outside the room, and the measured average level difference between a source room average and the transmitted levels at the same potential eavesdropper location. For a given combination of level difference and ambient noise level, the likelihood of transmitted speech being audible or intelligible can be related to the probability of higher speech levels occurring in the meeting room, based on the statistics of speech levels from a large number of meetings. For a particular meeting room speech level, there is an SPC value for which transmitted speech would be at the threshold of intelligibility or even at the threshold of audibility. One can create a set of increasing SPC values corresponding to increasing speech privacy and for each SPC value one can give the probability of transmitted speech being either audible or intelligible. This makes it possible to accurately specify speech privacy criteria for meeting rooms and offices, varying from conditions of quite minimal to extremely high speech privacy, with an associated risk of a speech privacy lapse which is acceptable for each situation.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".