ASHRAE RP 1852 toward a unified metric for speech privacy in high-performance buildings: Speech level variation by office environment and communication type
Bibliographic record
Abstract
The loudness of speech is critical in predicting speech intelligibility and privacy in office environments. The surrounding environment can influence speech levels, necessitating accurate measurement in typical work settings to enhance predictions of speech privacy. Standardized speech levels and spectra, as outlined in ASTM or ANSI standards, can aid in predicting speech privacy or intelligibility. However, these data are collected in anechoic chambers with participants following scripted scenarios. This study presents a revised and updated examination of speech levels in two offices in Quebec, Canada, analyzing data from over 70 employees across different room types, communication scenarios, languages, and tasks. In open offices, desks with partitions showed higher speech levels (56 dBA) compared to those without partitions (52 dBA). Meeting rooms showed relatively consistent levels (52-54 dBA) regardless of size. Teleconference group meetings resulted in employees using slightly higher levels (54 dBA) compared to other communication methods within the same rooms (53 dBA). Statistical analysis revealed significant effects of office type, communication method, language, and task on speech levels. Individual variations in speech were more significant than office layout or communication methods. The observed variability in speech levels across different individual speakers and office settings suggests that current standardized methods for assessing speech privacy may need re-evaluation.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".