Vector quantization of harmonic magnitudes for low-rate speech coders
Why this work is in the frame
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Bibliographic record
Abstract
Several techniques for speech coding at rates of 4 kb/s and lower require quantization of spectral magnitudes at a set of frequencies which are harmonics of the fundamental pitch period of the talker (for example: multiband excitation coding, sinusoidal transform coding, and time-frequency interpolation). The number of harmonic magnitudes to be quantized depends on the fundamental frequency value and hence is variable, changing from frame to frame. The variable number of components to be quantized makes it difficult to use fixed-dimension vector quantization for harmonic magnitude encoding. In this paper, we introduce a quantization technique called non-square transform vector quantization (NSTVQ) which uses a fixed-dimension vector quantizer combined with a variable-size non-square transform which maps the variable-dimension harmonic magnitude vector into a fixed-dimension vector. The optimal reconstruction procedure for non-square transforms is derived and shown to be equivalent to an optimal least-square estimation procedure. The proposed technique is evaluated experimentally as part of a new coding system called spectral excitation coding (SEC). The results are compared to an existing technique which estimates the spectral shape using all-pole modeling followed by vector quantization of the LSP parameters.
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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 | 0.001 | 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