Voice biometrics distinction between English and Arabic using Sound Cleaner Filtering and SpeechPro SIS II analysis
Bibliographic record
Abstract
Voice biometrics is the technology of audio sample examination and extraction of voice patterns to verify the speaker’s identity. Audio forensic experts enhance the quality of a questioned audio recording to analyze its unique voiceprint and compare it to an exemplar. This research evaluates the difference in articulation of consonant and vowel sounds between English and Arabic using SpeechPro Software, which branches into Sound Cleaner and SIS II. Sound Cleaner II is used to edit and filter the audio samples, and SIS II graphically analyzes and compares the speech signals and formants patterns. Research results suggest formant overlap for vowels A, I, and U, whereas no intersection is evident for the E and O vowels. Also, the research assesses the software’s voice recognition ability when studying and comparing audio samples of different language. Results suggest that SIS II is successful at linking both the examined English and Arabic exemplars to a single likely speaker match
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".