Objective analysis of the effect of memory inclusion on bandwidth extension of narrowband speech
Why this work is in the frame
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Bibliographic record
Abstract
For the purpose of improving Bandwidth Extension (BWE) of narrowband speech, we continue our recent work on the positive effect of exploiting the temporal correlation of speech on the dependence between speech frequency bands. We have shown that such memory inclusion into MFCC speech parametrization translates into higher highband certainty. In the work presented herein, we employ VQ to estimate highband discrete entropies, thus refining our analysis of the effect of memory inclusion on increasing highband certainty. Moreover, we extend our pre-vious analysis to LSF parameters. We further construct a BWE system that exploits our memory inclusion technique, thus trans-lating highband certainty gains into practical BWE performance improvement as measured by the objective quality of recon-structed speech. Results show that memory inclusion decreases the log-Spectral Distortion of the extended highband speech by as much as 1 dB corresponding to more than 14 % relative.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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