Optimum Reverberation for Speech Intelligibility for Normal and Hearing-Impaired Listeners in Realistic Classrooms Using Auralization
Bibliographic record
Abstract
The objective of this study was to use auralization techniques to investigate the optimal reverberation for speech intelligibility for normal-hearing and hearing-impaired adult listeners in classrooms with non-diffuse sound fields. This extended a previous study involving rooms with diffuse sound fields to more realistic rooms. Modified Rhyme Test (MRT) signals were auralized in six virtual classroom configurations with different reverberation times. Each classroom contained a speech source, a listener at a receiver position, and a noise source located between the talker and the listener. Two speech- and noise-source output-level differences (0 and +4 dB) were tested. Subjects performed speech-intelligibility tests in the virtual classrooms to identify the reverberation time that gave the best results in each case. For both normal and hearing-impaired listeners, the optimal reverberation time was generally non-zero, and increased with decreased speech-to-noise level difference. Hearing-impaired subjects apparently required more early energy than normal-hearing subjects. The optimal reverberation time for speech intelligibility in classrooms is not necessarily zero, as is commonly believed. The optimal value is generally non-zero, and varies with the room, the locations of the speech and noise sources and the listener, and the noise level.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".