Dual-LoRA with a language-anchored adversary achieves 0.91% EER on the TidyVoice benchmark for cross-lingual speaker verification by targeting true linguistic cues while preserving speaker discriminability.
Advancing speaker embedding learning: Wespeaker toolkit for research and produc- tion
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
eess.AS 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Cross-lifespan evaluation shows adult-trained speech foundation models degrade on child and older-adult data, with joint multi-age training and targeted adaptation improving robustness especially using Whisper encoder.
citing papers explorer
-
Dual-LoRA: Parameter-Efficient Adversarial Disentanglement for Cross-Lingual Speaker Verification
Dual-LoRA with a language-anchored adversary achieves 0.91% EER on the TidyVoice benchmark for cross-lingual speaker verification by targeting true linguistic cues while preserving speaker discriminability.
-
Exploring Speech Foundation Models for Speaker Diarization Across Lifespan
Cross-lifespan evaluation shows adult-trained speech foundation models degrade on child and older-adult data, with joint multi-age training and targeted adaptation improving robustness especially using Whisper encoder.