On the influence of regularization techniques on label noise robustness: Self-supervised speaker verification as a use case
Bibliographic record
Abstract
Clustering-based Pseudo-Labels (PLs) are widely used to optimize Speaker Embedding networks and train Self-Supervised Speaker Verification (SV) systems. However, this self-supervised training scheme relies on highly accurate PLs. In this paper, we perform a large investigative study of the effect of several regularization techniques (mixup, label smoothing, employing sub-centers) on the label noise robustness of self-supervised speaker verification systems. We study these techniques and apply them to various recent metric learning loss functions for better generalization of self-supervised speaker verification systems. In particular, we investigate the effect of these losses and regularizations on the robustness of the self-supervised SV task against label noise using various clustering models to generate real-world PLs of different noise patterns and levels. We provide a thorough comparative analysis of the generalization performance of these losses and regularization techniques using different numbers of clusters and propose some combination systems that are effective against label noise and lead to considerable improvements in SV performance.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".