Assessment of language impact to speech privacy in closed offices
Bibliographic record
Abstract
Speech privacy is the opposite concept of speech intelligibility and can be assessed by the predictors of speech intelligibility. Based on the existing standards and the research to date, most objective assessments for speech privacy and speech intelligibility, such as articulation index (AI) or speech intelligibility index (SII), speech transmission index (STI), and sound early-to-late ratio (C50), are evaluated by the subjective measurements. However, these subject measurements are based on the studies of English or the other Western languages. The language impact to speech privacy has been overseen. It is therefore necessary to study the impact of different languages and accents in multiculturalism environments to speech privacy. In this study, subjective measurements were conducted in closed office environments by using English and a tonal language, Mandarin. Detailed investigations on the language impact to speech privacy were carried out with the two languages. The results of this study reveal the significant evaluation variations in speech privacy when different languages are used. The subjective measurement results obtained in this study were also compared with the objective measurement employing articulation indices.
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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 | 0.015 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".