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
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks
Orchid Chetia Phukan, Devyani Koshal, Swarup Ranjan Behera, Arun Balaji Buduru, Rajesh Sharma
Guided Speaker Embedding
Shota Horiguchi, Takafumi Moriya, Atsushi Ando, Takanori Ashihara, Hiroshi Sato, Naohiro Tawara, Marc Delcroix
Disentangling Age and Identity with a Mutual Information Minimization Approach for Cross-Age Speaker Verification
Fengrun Zhang, Wangjin Zhou, Yiming Liu, Wang Geng, Yahui Shan, Chen Zhang
Enhancing Open-Set Speaker Identification through Rapid Tuning with Speaker Reciprocal Points and Negative Sample
Zhiyong Chen, Zhiqi Ai, Xinnuo Li, Shugong Xu
Speaker-IPL: Unsupervised Learning of Speaker Characteristics with i-Vector based Pseudo-Labels
Zakaria Aldeneh, Takuya Higuchi, Jee-weon Jung, Li-Wei Chen, Stephen Shum, Ahmed Hussen Abdelaziz, Shinji Watanabe, Tatiana Likhomanenko, Barry-John Theobald
Speaker Contrastive Learning for Source Speaker Tracing
Qing Wang, Hongmei Guo, Jian Kang, Mengjie Du, Jie Li, Xiao-Lei Zhang, Lei Xie