Deep bottleneck features for i-vector based text-independent speaker verification
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes the application of deep neural networks (DNNs), trained to discriminate among speakers, to improving performance in text-independent speaker verification. Activations from the bottleneck layer of these DNNs are used as features in an i-vector based speaker verification system. The features derived from this network are thought to be more robust with respect to phonetic variability, which is generally considered to have a negative impact on speaker verification performance. The verification performance using these features is evaluated on the 2012 NIST SRE core-core condition with models trained from a subset of the Fisher and Switchboard conversational speech corpora. It is found that improved performance, as measured by the minimum detection cost function (minDCF), can be obtained by appending speaker discriminative features to the more widely used mel-frequency cepstrum coefficients.
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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