Updating the SRMR-CI Metric for Improved Intelligibility Prediction for Cochlear Implant Users
Why this work is in the frame
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Bibliographic record
Abstract
When compared to intrusive speech intelligibility metrics, non-intrusive ones show a stronger dependency on speech content, given the lack of a reference signal for distortion level computation. Reduction of this dependency is an important step needed to develop reliable metrics. In this paper, two different updates to SRMR-CI, a recently-proposed speech intelligibility metric tailored for cochlear implant users, are applied. First, modulation energy thresholding is proposed to reduce the variability caused by the differences in modulation spectral representations for different phonemes and speakers, as well as speech enhancement algorithm artifacts. Second, a narrower range of modulation filters is employed to reduce fundamental frequency effects. Experimental results show that the updated metric outperforms two benchmark metrics, namely ModA and ANIQUE+, by as much as 15% in terms of correlation between objective and subjective ratings, and a relative decrease of 47% in root mean square error compared to the previously-proposed SRMR-CI metric.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it