  State-of-the-art speaker verification models are based on deep learning
techniques, which heavily depend on the handdesigned neural architectures from
experts or engineers. We borrow the idea of neural architecture search(NAS) for
the textindependent speaker verification task. As NAS can learn deep network
structures automatically, we introduce the NAS conception into the well-known
x-vector network. Furthermore, this paper proposes an evolutionary algorithm
enhanced neural architecture search method called Auto-Vector to automatically
discover promising networks for the speaker verification task. The experimental
results demonstrate our NAS-based model outperforms state-of-the-art speaker
verification models.
