Joint optimization of short-term and long-term predictors in CELP speech coders
Bibliographic record
Abstract
The objective of this work is to investigate whether joint optimization of short-term and long-term predictors manifests significant advantages over the sequential optimization in speech coding. We propose a new joint optimization method based on Wiener filtering. The proposed analysis model resolves the pitch-bias problem of classical LPC analysis by considering the contribution of the long-term predictor while optimizing the short-term predictor. Our approach to joint optimization is based on analysis-by-synthesis and guarantees the synthesis filter stability. By applying our proposed joint optimization approach to CELP coding we obtain superior objective and subjective performance relative to CELP coding with sequential optimization. To provide voice quality equivalent to that of sequentially optimized CELP, the jointly optimized coder needs fewer FCB pulses and requires a reduced bit budget for LPC quantization. Our listening tests suggest that the JCELP coder at 4.25 kbps is equivalent in quality to the G.729 at 8 kbps.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".