Modelling speaker and channel variability using deep neural networks for robust speaker verification
Why this work is in the frame
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Bibliographic record
Abstract
We propose to improve the performance of i-vector based speaker verification by processing the i-vectors with a deep neural network before they are fed to a cosine distance or probabilistic linear discriminant analysis (PLDA) classifier. To this end we build on an existing model that we refer to as Non-linear Within Class Normalization (NWCN) and introduce a novel Speaker Classifier Network (SCN). Both models deliver impressive speaker verification performance, showing a 56% and 68% relative improvement over standard i-vectors when combined with a cosine distance backend. The NWCN model also reduces the equal error rate for PLDA from 1.78% to 1.63%. We also test these models under the constraints of domain mismatch, i.e. when no in-domain training data is available. Under these conditions, SCN features in combination with cosine distance performs better than the PLDA baseline, achieving an equal error rate of 2.92% as compared to 3.37%.
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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 | 0.000 |
| 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 it