Prediction of binaural speech intelligibility when using non-linear hearing aids.
Bibliographic record
Abstract
A new objective measurement system is proposed to predict speech intelligibility in binaural listening conditions for use with hearing aids. Digital processing inside a hearing aid often involves non-linear operations such as clipping, compression, and noise reduction algorithms. Standard objective measures such as the articulation index, the speech intelligibility index (SII), and the speech transmission index have been developed for monaural listening. Binaural extensions of these measures have been proposed in the literature, essentially consisting of a binaural pre-processing stage followed by monaural intelligibility prediction using the better ear or the binaurally enhanced signal. In this work, a three-stage non-linear extension of the binaural SII approach is introduced consisting of (1) a stage to deal with non-linear processing based on a simple signal separation scheme to recover estimates of speech and noise signals at the output of hearing aids [Hagerman and Olofsson, Acust. Acta Acust. 90, 356 (2004)], (2) a binaural processing stage using the equalization-cancellation model [Beutelmann and Brand, J. Acoust. Soc. Am. 120, 331 (2006)], and (3) a stage for intelligibility prediction using the monaural SII [ANSI-S3.5, 1997 (R2007)]. Details of the new procedure will be discussed. [Research supported by NSERC (Canada).]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".