Speech level variation by office environment and communication type
Bibliographic record
Abstract
The loudness of speech is crucial for predicting speech privacy in offices, with surrounding environments influencing speech levels. While ASTM and ANSI standards provide reference data, these are based on scripted scenarios in anechoic chambers. This study examined speech levels in two real offices with over 70 employees, considering various room types, communication scenarios, languages, and tasks. Key findings include: (1) Open-plan offices with partitioned desks showed employees used higher speech levels compared to those without partitions. (2) Meeting rooms showed relatively consistent speech levels regardless of size. (3) Teleconference group meetings resulted in employees using marginally higher average speech levels compared to other communication methods within the same rooms. (4) Statistical analysis revealed significant effects of office type, communication method, and task on speech levels. (5) There is more significant variation in individual speech levels among workers than due to office type or communication method used. Additionally, the measured speech levels in this study were lower than those measured in anechoic room studies. The findings suggest that current standardized methods for assessing speech privacy may need reevaluation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".