Explore Long-Range Context feature for Speaker Verification

Abstract

Capturing long-range dependency and modeling long temporal contexts is proven to benefit speaker verification tasks. In this paper, we propose the combination of the Hierarchical-Split block(HS-block) and the Depthwise Separable Self-Attention(DSSA) module to capture richer multi-range context speaker features from a local and global perspective respectively. Specifically, the HS-block splits the feature map and filters into several groups and stacks them in one block, which enlarges the receptive fields(RFs) locally. The DSSA module improves the multi-head self-attention mechanism by the depthwise-separable strategy and explicit sparse attention strategy to model the pairwise relations globally and captures effective long-range dependencies in each channel. Experiments are conducted on the Voxceleb and SITW. Our best system achieves 1.27% EER on the Voxceleb1 test set and 1.56% on SITW by applying the combination of HS-block and DSSA module.