Investigation Of The Quality Of Pseudo-Labels For The Self-Supervised Speaker Verification Task
Bibliographic record
Abstract
Optimizing a speaker embedding network in a discriminative fashion using clustering algorithm-driven pseudo-labels is one of the most widely used self-supervised speaker verification system training schemes. Although this kind self-supervised supervised training scheme showed impressive performance, recent studies have shown that label noise can significantly impact the performance. In this contribution, we explore various clustering algorithms to generate speaker pseudo-labels and conduct a fine-grained analysis on the relationship between the quality of the pseudo-labels and the speaker verification performance. Through experimental results, we also shed light on several previously unexplored and overlooked aspects of the pseudo-labels that can have an impact on the speaker verification performance. Furthermore, we observe that the performance of a self-supervised speaker verification system relies heavily on multiple qualitative aspects of the clustering algorithm used to generate the pseudo-labels. Additionally, we show that severe reduction in speaker verification performance can occur from overfitting to the noisy pseudo-labels and that the mixup data augmentation strategy can mitigate the memorization effects of label noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".