Speaker Verification
Speaker verification (SV) aims to automatically authenticate a person's identity based on their voice, focusing on creating robust and accurate systems. Current research emphasizes improving the discriminative power of speaker embeddings through techniques like contrastive learning, disentangling confounding factors such as age and channel variations, and leveraging powerful pre-trained models such as WavLM and Whisper. These advancements are crucial for enhancing security in various applications, from access control to forensic investigations, and are driving ongoing efforts to improve robustness against spoofing attacks and noisy conditions.
Papers
AdvSV: An Over-the-Air Adversarial Attack Dataset for Speaker Verification
Li Wang, Jiaqi Li, Yuhao Luo, Jiahao Zheng, Lei Wang, Hao Li, Ke Xu, Chengfang Fang, Jie Shi, Zhizheng Wu
An Initial Investigation of Neural Replay Simulator for Over-the-Air Adversarial Perturbations to Automatic Speaker Verification
Jiaqi Li, Li Wang, Liumeng Xue, Lei Wang, Zhizheng Wu
Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification
Ze Li, Yuke Lin, Ning Jiang, Xiaoyi Qin, Guoqing Zhao, Haiying Wu, Ming Li
VoiceExtender: Short-utterance Text-independent Speaker Verification with Guided Diffusion Model
Yayun He, Zuheng Kang, Jianzong Wang, Junqing Peng, Jing Xiao
Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Automatic Speaker Verification
Duc-Tuan Truong, Ruijie Tao, Jia Qi Yip, Kong Aik Lee, Eng Siong Chng
Rethinking Session Variability: Leveraging Session Embeddings for Session Robustness in Speaker Verification
Hee-Soo Heo, KiHyun Nam, Bong-Jin Lee, Youngki Kwon, Minjae Lee, You Jin Kim, Joon Son Chung