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
Speaker Verification in Multi-Speaker Environments Using Temporal Feature Fusion
Ahmad Aloradi, Wolfgang Mack, Mohamed Elminshawi, Emanuël A. P. Habets
Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector
Jungwoo Heo, Ju-ho Kim, Hyun-seo Shin
Personalized Keyword Spotting through Multi-task Learning
Seunghan Yang, Byeonggeun Kim, Inseop Chung, Simyung Chang
Domain Agnostic Few-shot Learning for Speaker Verification
Seunghan Yang, Debasmit Das, Janghoon Cho, Hyoungwoo Park, Sungrack Yun
Learning from human perception to improve automatic speaker verification in style-mismatched conditions
Amber Afshan, Abeer Alwan