On the influence of the quality of pseudo-labels on the self-supervised speaker verification task: a thorough analysis
Bibliographic record
Abstract
One of the most widely used self-supervised (SS) speaker verification (SV) system training methods is to optimize the speaker embedding network in a discriminative fashion using clustering algorithm (CA)-driven Pseudo-Labels (PLs). Although the PL-based SS training scheme showed impressive performance, recent studies have shown that label noise can significantly impact performance. In this paper, we have explored various PLs driven by different CAs and conducted a fine-grained analysis of the relationship between the quality of the PLs and the SV performance. Experimentally, we shed light on several previously overlooked aspects of the PLs that can impact SV performance. Moreover, we could observe that the SS-SV performance is heavily dependent on multiple qualitative aspects of the CA used to generate the PLs. Furthermore, we show that SV performance can be severely degraded from overfitting the noisy PLs and that the mixup strategy can mitigate the memorization effects of label noise.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".