A Phonetic Analysis of Speaker Verification Systems through Phoneme selection and Integrated Gradients
Bibliographic record
Abstract
Speaker recognition systems are usually crafted to identify or verify the identity of a given speaker independently of the linguistic content contained in the utterance used. We use two explainability techniques to analyze the impact of phonetic variations on a speaker verification system using VoxCeleb. We use Whisper and the Montreal Forced Aligner (MFA) to transcribe, then segment phonetically the Voxceleb1 test set. Phoneme selection is first used, before computation of the x-vectors, to observe which phonemes are the most discriminative through their impact on EER and MinDCF metrics. Integrated Gradients are then used to show which phonemes yielded the highest gradients comparing two speakers. We find that for the representation of the x-vector in speaker recognition systems, both consonants and vowels are relevant and important to capture the distinctive \ncharacteristics of a speaker’s voice and generate effective and discriminative representations.
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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 | 0.002 |
| 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".