A study on speech privacy in closed offices
Bibliographic record
Abstract
Quantification of speech privacy in closed spaces is dependent on several factors: attenuation and absorption characteristics of the construction, background sound levels, speaking levels of the talker, the language and dialect, as well as accent used in the speech. Speech privacy is the opposite concept of speech intelligibility and can be assessed by the predictors of speech intelligibility. In this paper, several assessment methods of speech intelligibility are introduced. They are adapted to measure the speech privacy in closed offices, including standard ASTM E1130, speech transmission index (STI) and early-to-late ratio (clarity). The experimental results are used to evaluate the speech privacy index (PI), which is the rating number proposed for assessing the speech privacy. The subjective measurements offered by human subjects are also conducted using different languages: English and Mandarin Chinese. The test materials used in the speech intelligibility testing are single words (MRT words), sentences and conversations. Both measurements are described under the same acoustical environment. The results of the subjective and objective measurements indicate that the current standard needs some modifications when it is used in closed offices.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".