A new measure for assessment of architectural speech security
Bibliographic record
Abstract
A new measure has been developed to indicate the audibility and intelligibility of speech transmitted through walls from adjacent rooms. The new measure is a frequency-weighted signal-to-noise ratio. It is shown to be a more accurate predictor of subjective ratings than the Articulation Index, the Speech Intelligibility Index, and simple A-weighted signal-to-noise ratios. Listening tests using English sentences were conducted to measure the fraction of words intelligible to acute-hearing, native-English-speaking listeners under a range of acoustical conditions. Each subject listened to 500 sentences, each of which had been filtered to represent passage through some type of wall construction. The level of the speech, as well as the level and spectrum of background ventilation-type noise was varied. The conditions in the test ranged from those where all subjects were able to correctly identify all the words in a sentence (i.e., very poor security), through to those where all subjects were unable to even recognize the presence of a speaking voice (i.e., excellent security). The tests, analysis, and derivation of the measure will be discussed, as will the directions of ongoing work in the area.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".