Predicting speech intelligibility in real-world noise environments from functional measures of hearing
Bibliographic record
Abstract
In everyday life situations and in many occupational settings, speech communication is often performed in noisy environments. These environments can sometimes be very challenging, particularly for individuals impaired by hearing loss. Diagnostic measures of hearing, such as the audiogram, are not adequate to make accurate predictions of speech intelligibility in real-world noise environments. Instead, a direct functional measure of hearing, the Hearing In Noise Test (HINT), has been identified and validated for use in predicting speech intelligibility in a wide range of face-to-face speech communication situations in real-world noise environments. The prediction approach takes into account the voice level of the talker in noise due to the Lombard effect, the communication distance between the talker and the listener, a statistical model of speech perception in specific noises, and the functional hearing abilities of the listener. The latter is taken as the elevation of the individual’s speech reception threshold in noise above the normative value for the HINT test. This test is available in several languages, so that language-specific needs can be addressed. The detailed approach will be presented with an emphasis placed on application examples in clinical and/or occupational settings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".