LSP quantization by a union of locally trained codebooks
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Bibliographic record
Abstract
We present a fixed rate encoding scheme for the line spectrum pair (LSP) representation of an LPC-filter, based on Gaussian mixture (GM) modeling. For each mixture component, we construct a codebook by a union of product quantizers. Each local codebook is trained, independently, using a clustering scheme similar to the generalized Lloyd algorithm (GLA), over synthetic data. The training algorithm iterates fast, due to low complexity encoding, and converges in few iterations. The overall codebook is a combination of local codebooks, and inherits their high performance, while having a moderate complexity. We provide numerical results for average spectral distortion (SD) of the proposed encoder, and benchmark them by a lower bound, according to high-rate theory. We achieve an average SD (full-band measure) of 1 dB at 23 b/frame, for speech signals sampled at 8 kHz and LPC of order 10. By tolerating additional complexity, we reach a SD within 0.01 dB of the lower bound.
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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 | 0.000 | 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