PnP reformulates adversarial purification as learning positive-incentive noise to defend speaker verification against attacks with high efficiency and limited impact on genuine utterances.
X- vectors: Robust dnn embeddings for speaker recognition
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.AS 3years
2026 3roles
background 1polarities
background 1representative citing papers
PhiNet adds phonetic interpretability to speaker verification while matching the accuracy of standard black-box models on VoxCeleb, SITW, and LibriSpeech.
ProPS uses a mixture density network conditioned on SBERT text embeddings to generate Gaussian mixture models over speaker x-vectors from natural language profile descriptions.
citing papers explorer
-
Positive-Incentive Noise Predictor for Adversarial Purification in Speaker Verification
PnP reformulates adversarial purification as learning positive-incentive noise to defend speaker verification against attacks with high efficiency and limited impact on genuine utterances.
-
PhiNet: Speaker Verification with Phonetic Interpretability
PhiNet adds phonetic interpretability to speaker verification while matching the accuracy of standard black-box models on VoxCeleb, SITW, and LibriSpeech.
-
ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
ProPS uses a mixture density network conditioned on SBERT text embeddings to generate Gaussian mixture models over speaker x-vectors from natural language profile descriptions.