Using listening difficulty ratings of conditions for speech communication in rooms
Bibliographic record
Abstract
The use of listening difficulty ratings of speech communication in rooms is explored because, in common situations, word recognition scores do not discriminate well among conditions that are near to acceptable. In particular, the benefits of early reflections of speech sounds on listening difficulty were investigated and compared to the known benefits to word intelligibility scores. Listening tests were used to assess word intelligibility and perceived listening difficulty of speech in simulated sound fields. The experiments were conducted in three types of sound fields with constant levels of ambient noise: only direct sound, direct sound with early reflections, and direct sound with early reflections and reverberation. The results demonstrate that (1) listening difficulty can better discriminate among these conditions than can word recognition scores; (2) added early reflections increase the effective signal-to-noise ratio equivalent to the added energy in the conditions without reverberation; (3) the benefit of early reflections on difficulty scores is greater than expected from the simple increase in early arriving speech energy with reverberation; (4) word intelligibility tests are most appropriate for conditions with signal-to-noise (S/N) ratios less than 0 dBA, and where S/N is between 0 and 15-dBA S/N, listening difficulty is a more appropriate evaluation tool.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".