Speaker Recognition

Speaker recognition, the automated identification of individuals based on their voice, aims to develop robust and accurate systems for various applications, from security access to personalized services. Current research focuses on improving model robustness against noise and variations in speaking style, exploring architectures like ResNet, transformer networks, and graph neural networks, and leveraging self-supervised learning and data augmentation techniques to enhance performance. These advancements are crucial for addressing challenges like spoofing detection, cross-channel consistency, and privacy concerns, ultimately leading to more reliable and ethically sound speaker recognition technologies.