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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".