ASHRAE RP 1852 toward a unified metric for speech privacy in high-performance buildings: Determination of a suitable metric for both open-plan and closed offices through measurement
Bibliographic record
Abstract
Various metrics have been developed since the 1960s to measure speech privacy (SP) within office spaces, but have diverged between those intended for open-plan (e.g., Articulation Index, ASTM E1130 or Speech Transmission Index, ISO 3382-3) or those for closed offices/rooms (e.g., Speech Privacy Potential, or Speech Privacy Class, SPC, ASTM E2638). Others (e.g., Speech intelligibility Index, ANSI S3.5) do not have a standardized use in offices. No single metric has been validated for both office types. Furthermore, SII, AI, and STI struggle to differentiate between unintelligibility and inaudibility, while SPC’s relationship to distraction and use in open plan is underexplored. This ASHRAE-funded research project (RP 1852) aimed to determine and validate a single metric and method for both space types to accurately rate or predict SP (in terms of audibility, intelligibility, and distraction). 308 open-plan workstations and 44 closed rooms (private offices and meeting rooms) across 5 sites were assessed using the aforementioned metrics and associated methods and variants thereof. Statistical analysis of 1312 open-plan and 1532 closed room measurements reveals the relations between SP metrics. Effects of source/receiver locations and room boundaries are described. The strengths and weaknesses are discussed, with ultimately the SPC being the strongest candidate for a unifying metric across spaces.
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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".