Variability of speech spectra in offices by room types and communication methods
Bibliographic record
Abstract
The acoustic characteristics of room environments can significantly influence speech levels and spectra, an aspect often overlooked in the current method for predicting speech privacy. This paper presents an investigation into the variability of speech spectra in various office contexts, with a specific focus on the influence of room type, communication medium, and language. The study involved over 70 workers in different office room types in Quebec, Canada, who participated in measuring speech spectra within those spaces. Two communication methods, in-person and video-conferencing scenarios, were utilized with participants using either English or French languages. The real-world scenarios shed light on the significant impact of various parameters on speech characteristics within office settings. Comparing the results with standardized speech levels and spectra from ASTM and ISO standards, derived from controlled environments like anechoic chambers, the study reveals significant differences when applying the standard speech spectra to actual office environments. Based on these findings, the study proposes potential modifications to the existing standards, aiming to enhance the accuracy of speech privacy and intelligibility predictions.
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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 | 0.008 |
| 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 | 0.000 |
| 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".