Well-calibrated heavy tailed Bayesian speaker verification for microphone speech
Bibliographic record
Abstract
The work presented in this paper is an extension of our two previous works. In the first paper, we proposed a low dimensional feature (i-vectors) extractor which is suit able for both telephone and microphone data of the NIST speaker recognition evaluation dataset. The second paper introduces the use of Probabilistic Linear Discriminant Analysis (PLDA) framework with a heavy tailed distribution for speaker verification. The advantage of PLDA comes from the fact that it does not require eigenchannel modelization nor scores normalization. However, this approach is only known for its success on telephone data speech but not for micro phone data. We propose to overcome this drawback by using PLDA as a second pass at the front-end feature extraction as well as a classifier. We present results on female speakers for the interview-interview condition in NIST2010 SRE. As measured by equal error rate (ERR) and NIST detection cost function (DCF), results with raw scores are 17% better than with score normalization. We have also calibrated our scores and we achieve a minimum and an actual DCF respectively of 0.559 and 0.607.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".