Neural code-excited linear prediction for low power speech compression
Bibliographic record
Abstract
In this paper, we discuss the use of artificial neural learning methods for low bit-rate speech compression, potentially in non-stationary environments. Unsupervised learning algorithms are particularly well-suited for vector quantization (VQ) which is used in many speech compression applications. We discuss two unsupervised learning algorithms: frequency-sensitive competitive learning and Kohonen's self-organizing maps which have both been investigated for learning the codebook vectors in an adaptive vector quantizer. In contrast with earlier work, we have employed these learning rules in VQ of the linear predictive coding (LPC) prediction residual. The performance of these unsupervised learning algorithms in speaker-dependent and speaker-independent speech compression are presented. Our results compare favourably with those of code-excited linear prediction (CELP) requiring reduced computational power with a tolerable reduction in speech quality. We also explore the effects of limited precision on classification and learning in competitive learning algorithms for low power VLSI implementations.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".