CAMSAT: Augmentation Mix and Self-Augmented Training Clustering for Self-Supervised Speaker Recognition
Bibliographic record
Abstract
Clustering (CL)-based pseudo-labels (PLs) are widely used to optimize speaker embedding (SE) networks and train self-supervised (SS) speaker verification (SV) systems. However, PL-based SS training depends on high-quality PLs. In this paper, we propose a general-purpose CL algorithm called CAMSAT that outperforms all other baselines used to cluster SEs. Moreover, using the generated PLs to train our SE system allows us to further improve SV performance. CAMSAT is based on two principles: (1) mixing predictions of augmented samples to provide a complementary supervisory signal for CL and enforce symmetry within augmentations (2) Self-Augmented Training to enforce representation invariance and maximize the information-theoretic dependency between samples and their predicted PLs. We provide a thorough comparative analysis of the performance of our CL method vs. all baselines using a variety of CL metrics and perform an ablation study to analyze the contribution of each component.
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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 | 0.000 | 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 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".