Predicting phoneme and word recognition in noise using a computational model of the auditory periphery
Bibliographic record
Abstract
Several filterbank-based metrics have been proposed to predict speech intelligibility (SI). However, these metrics incorporate little knowledge of the auditory periphery. Neurogram-based metrics provide an alternative, incorporating knowledge of the physiology of hearing by using a mathematical model of the auditory nerve response. In this work, SI was assessed utilizing different filterbank-based metrics (the speech intelligibility index and the speech-based envelope power spectrum model) and neurogram-based metrics, using the biologically inspired model of the auditory nerve proposed by Zilany, Bruce, Nelson, and Carney [(2009), J. Acoust. Soc. Am. 126(5), 2390-2412] as a front-end and the neurogram similarity metric and spectro temporal modulation index as a back-end. Then, the correlations with behavioural scores were computed. Results showed that neurogram-based metrics representing the speech envelope showed higher correlations with the behavioural scores at a word level. At a per-phoneme level, it was found that phoneme transitions contribute to higher correlations between objective measures that use speech envelope information at the auditory periphery level and behavioural data. The presented framework could function as a useful tool for the validation and tuning of speech materials, as well as a benchmark for the development of speech processing algorithms.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".