A study on dimensions of feature space for text-independent speaker verification systems
Bibliographic record
Abstract
This paper studies the effect of feature dimensions on an MFCC/IMFCC-GMM based text-independent speaker verification system (SVS). A typical baseline system is used to evaluate the impact of features based on the number of Mel, inverted Mel, delta and double delta coefficients while keeping other system parameters constant for all experiments. The relevance of the spectral information contained in the features according to their discrimination power was assessed through a GMM-UBM system with the TIMIT corpus. A new scoring method is reported in which the fusion of feature likelihoods is conducted before the UBM normalization. The study shows that features carrying high frequency spectral content have high information gain enabling better performance of the SVS. Similarly, adding more coefficients of MFCCs and IMFCCs instead of dynamic features such as delta and double delta coefficients improves the SVS's equal error rate (EER). Our scoring technique outperformed the traditional scoring algorithm by 9.7%.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".