Speech and noise levels for predicting the degree of speech security
Bibliographic record
Abstract
A meeting room is speech secure when it is difficult or impossible for an eavesdropper to overhear speech from within. The degree of security could range from less stringent conditions of being barely able to understand a few words from the meeting room, to higher levels, where transmitted speech would be completely inaudible. This paper reports on measurements to determine the statistical distribution of speech levels in meeting rooms and the distribution of ambient noise levels just outside meeting rooms. To select the required transmission characteristics for a meeting room wall, one would first decide on an acceptable level of risk, in terms of the probability of a speech security lapse occurring. This leads to the selection of a combination of a speech level in the meeting room and a noise level nearby that would occur together with this probability. The combination of appropriate estimates of meeting room speech levels and nearby ambient noise levels, together with the sound transmission characteristics of the intervening partition, makes it possible to calculate signal/noise ratio indices related to speech security [J. Acoust. Soc. Am. 116(6), 3480–3490 (2004)]. The value of these indices indicates if adequate speech security will be achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".