Multi Channel Speaker Verification

Multi-channel speaker verification aims to improve the accuracy of identifying speakers from recordings captured by multiple microphones, addressing challenges posed by noisy or reverberant environments. Current research focuses on developing robust models, such as graph convolutional networks and those incorporating optimal transport, to effectively fuse information across channels and time, often including techniques for channel selection and speech enhancement. These advancements are crucial for improving the performance of speaker verification systems in real-world applications, such as security systems and voice assistants, where multi-channel audio is common. The creation of standardized datasets, like MultiSV, is also driving progress by providing consistent benchmarks for evaluating these systems.