Model of Thailand Speech Intelligibility (T-SI) in the Large Classrooms from Public University
Bibliographic record
Abstract
The objectives of this research are to evaluate acoustic environments and to forecast STI values from spatial component variables in the large classrooms of the Thai public university that were specially controlled the same room finishing materials including the floor, walls, and ceiling. Whereas the five spatial component factors included (1) Room Volume (RV), (2) Ceiling Height (CH), (3) the Ratio of Depth to Width (Rdw), (4) Total Room Surface (TS), and (5) Percentage of Absorbing Surface areas (PAS). The research tools were the smartphones that used the applications for acoustical evaluation and speech intelligibility analysis. The Speech Transmission Index (STI), Reverberation Time (RT), and Background Noise Level (BNL) were collected by the calibrated microphone in the nine points distributed across the entire room. And also, the sounds for testing were simulated such as balloon burst, and STIPA signal via a sound generator. The Thailand Speech Intelligibility (T-SI) model was developed by the multiple regression analysis with a statistical at a confidence level of 95%.The results showed that this T-SI model depended on the strongly positive relationship of PAS and the slightly positive relationship of CH, TS while the RV, Rdw were slightly the negative relationship and which predicted STI values. Moreover, the highest affecting variable of T-SI model was CH and the lowest was PAS. However, this research implies that the improving room acoustic quality would be adjusting the sound absorbing surface areas i.e., increase the cloth curtain or appropriate methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".