Speaker Identification for Disguised Voices Based on Modified SVM Classifier
Bibliographic record
Abstract
Since voice disguise forms a significant threat in the plethora of illegal applications, it is essential to be able to identify the unknown speaker. This work focuses on scheming a modified Support Vector Machine (SVM) as a classifier to enhance the degraded speaker identification performance for disguised voices under an extreme high-pitched condition in a neutral talking environment. This research utilizes three different speech datasets: Arabic Emirati-accented database, “Speech Under Simulated and Actual Stress” (SUSAS) English database, and “Ryerson Audio-Visual Database of Emotional Speech and Song” (RAVDESS) English database. Our results show that modified SVM reports an average speaker identification performance for disguised voices equal to 93.95%. Our work demonstrates that modified SVM is superior to other classical classifiers such as: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Naïve Bayes (NB), and the conventional SVM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".