Towards objective measures of speech intelligibility for Cochlear Implant users in reverberant environments
Bibliographic record
Abstract
This study validates a novel approach to predict speech intelligibility for Cochlear Implant users (CIs) in reverberant environments. More specifically, we explore the use of existing objective quality and intelligibility metrics, applied directly to vocoded speech degraded by room reverberation, here assessed at ten different reverberation time (RT60) values: 0 s, 0.4 s - 1.0 s (0.1 s increments), 1.5 s and 2 s. Eight objective speech intelligibility predictors (SIPs) were investigated in this study. Of these, two were non-intrusive (i.e. did not require a reference signal) audio quality measures, four were intrusive, and two were intrusive speech intelligibility indexes. Three types of vocoders were implemented to examine how speech intelligibility predictions depended on the vocoder type. These were: noise-excited vocoder, tone-excited vocoder and a FFT-based N-of-M vocoder. Experimental results show that several intrusive quality and intelligibility measures were highly correlated with exponentially fit CI intelligibility data. On the other hand, only a recently - developed non-intrusive measure showed high correlations. These evaluations suggest that CI intelligibility may be accurately assessed via objective metrics applied to vocoded speech, thus may reduce the need for expensive and time-consuming listening tests.
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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 | 0.009 |
| 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 | 0.001 |
| 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".