Nonintrusive speech quality evaluation using an adaptive neurofuzzy inference system
Why this work is in the frame
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Bibliographic record
Abstract
This letter presents a novel nonintrusive speech quality evaluation method using an adaptive neurofuzzy inference system (ANFIS). The proposed method employed a first-order Sugeno-type fuzzy inference system (FIS) to estimate speech quality using only the output signal of the system under test. This new method was compared with the state-of-the-art nonintrusive quality evaluation standard, the ITU-T P.563 Recommendation, using seven subjective quality databases of the ITU-T P-series Supplementary 23. Experimental results show that the correlation of the proposed method with the subjective quality scores reached 0.8812, with a standard error of 0.3647 across the entire database. This compares favorably with the standard P.563, which provides a correlation and standard error of 0.8422 and 0.4493, respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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