On the influence of metric learning loss functions for robust self-supervised speaker verification to label noise
Bibliographic record
Abstract
While clustering-driven Pseudo-Labels (PLs) are commonly employed to optimize Speaker Embedding (SE) networks and facilitate training of self-supervised Speaker Verification (SV) systems, the efficacy of PL-based self-supervised training hinges on the accuracy of these generated labels. In this paper, we perform a large-scale comparative study of a wide range of recent metric learning loss functions for better generalization of self-supervised SV systems. In particular, we investigate the effect of these losses on the robustness of the self-supervised SV task against label noise using various real-life clustering-based PLs. We present an extensive comparative evaluation of the performance of these loss functions using different numbers of clusters and show that our proposed selection of loss functions is effective against label noise and leads to considerable improvements in SV performance. Moreover, using our selected losses combined with the adopted CAMSAT clustering algorithm-based PLs to train our SE system allows us to achieve state-of-the-art self-supervised SV performance. Code of our experiments will be made publicly available.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 | 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".