ClassTalk system for predicting and visualizing speech in noise in classrooms
Bibliographic record
Abstract
This paper discusses the ClassTalk system for modeling, predicting and visualizing speech in noise in classrooms. Modeling involves defining the classroom geometry, sources, sound-absorbing features, and receiver positions. Empirical models, used to predict speech and noise levels, and reverberation times, are described. Male or female speech sources, and overhead-, slide-, or LCD-projector, or ventilation-outlet noise sources, can have four output levels; values are assigned based on ranges of values found from published data and measurements. ClassTalk visualizes the floor plan, speech- and noise-source positions, and the receiver position. The user can walk through the room at will. In real time, six quantities—background-noise level, speech level, signal-to-noise level difference, useful-to-detrimental energy fraction (U50), Speech Transmission Index, and speech intelligibility—are displayed, along with occupied and unoccupied reverberation times. An example of a large classroom before and after treatment is presented. The future development of improved prediction models and of the sound module, which will auralize speech in noise with reverberation, is discussed.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".