Far Field Speaker Verification

Far-field speaker verification (FFSV) aims to reliably identify speakers from recordings made at a distance, a challenging task due to increased noise and reverberation. Current research focuses on improving robustness by incorporating phonetic information into speaker embeddings, employing multi-channel signal processing and deep neural network (DNN) architectures like ResNets and RepVGGS, and developing sophisticated training strategies such as transfer learning and self-supervised learning with large-scale datasets to mitigate data scarcity and overfitting. These advancements are crucial for enhancing the accuracy and reliability of speaker recognition in real-world applications like voice assistants and security systems.