Investigation of the optimum acoustical conditions for speech using auralization
Bibliographic record
Abstract
Speech intelligibility is mainly affected by reverberation and by signal-to-noise level difference, the difference between the speech-signal and background-noise levels at a receiver. An important question for the design of rooms for speech (e.g., classrooms) is, what are the optimal values of these factors? This question has been studied experimentally and theoretically. Experimental studies found zero optimal reverberation time, but theoretical predictions found nonzero reverberation times. These contradictory results are partly caused by the different ways of accounting for background noise. Background noise sources and their locations inside the room are the most detrimental factors in speech intelligibility. However, noise levels also interact with reverberation in rooms. In this project, two major room-acoustical factors for speech intelligibility were controlled using speech and noise sources of known relative output levels located in a virtual room with known reverberation. Speech intelligibility test signals were played in the virtual room and auralized for listeners. The Modified Rhyme Test (MRT) and babble noise were used to measure subjective speech intelligibility quality. Optimal reverberation times, and the optimal values of other speech intelligibility metrics, for normal-hearing people and for hard-of-hearing people, were identified and compared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".