Comparing testing methodologies of speech privacy class in closed offices
Bibliographic record
Abstract
Speech privacy is a principal research area in speech communication. The term "speech privacy" is generally interpreted a condition where speech cannot be readily understood by, but may be audible to, an unintended listener. The need to prevent sound from intruding into adjacent spaces in both closed and open-plan settings is a concern in various office buildings. Speech privacy is essentially a function of the signal-to-noise ratio, comprising the noise reduction between source and receiver positions, and the masking effective of background noise. Speech Privacy Class (SPC) is one of the commonly used metrics for speech privacy assessment in closed plan offices in North America. However, there is a reluctance to use SPC between closed rooms due to laborious testing requirements as per ASTM E2638; there is a supposed preference to use either Speech Privacy Potential (SPP) based on simpler NIC testing, or to use Articulation Index (AI) despite the AI being created solely for testing open-plan settings. The ASTM E2638-10 standard SPC testing procedure does not assume a diffuse field in the receiving space but rather evaluates the performance at each potential eavesdropping location on the basis that there may be an intentional listener. Thus, to better apply the SPC to unintended listening and speech privacy in typical commercial spaces, the goal is to compare the current methodology as per ASTM E2638-10 with alternative sampling methods considering the talker location and unintended listening positions outside the source room.
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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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".