Auralization study of optimum reverberation for speech intelligibility for normal and hearing-impaired listeners
Bibliographic record
Abstract
Reverberation and signal-to-noise level difference are two major factors affecting speech intelligibility. They interact in rooms. Past work has accounted for noise using a constant received background-noise level. Noise is actually generated by sources, and varies, and affects speech intelligibility differently, throughout the classroom, depending on where the sources are located. Here, a speech-babble noise source located at different positions in the room was considered. The relative output levels of the speech and noise sources, resulting in different signal-to-noise level differences, were controlled, along with the reverberation. The binaural impulse response of a virtual idealized classroom model was convolved with the Modified Rhyme Test (MRT) source and babble-noise signals in order to find the optimal configuration for speech intelligibility. Speech-intelligibility tests were performed with normal and hard-of-hearing subjects in each of 16 conditions which were combinations of reverberation time, signal-to-noise level difference, and speech- and noise-source locations. For both normal and hearing-impaired subjects, when the speech source was closer to the listener than the noise source, the optimal RT was zero. When the noise source was closer to the listener than the speech source, the optimal RT was generally non-zero. This agrees with theoretical results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".