  DNN-based speaker verification (SV) models demonstrate significant
performance at relatively high computation costs. Model compression can be
applied to reduce the model size for lower resource consumption. The present
study exploits weight quantization to compress two widely-used SV models,
namely ECAPA-TDNN and ResNet. Experimental results on VoxCeleb show that weight
quantization is effective for compressing SV models. The model size can be
reduced multiple times without noticeable degradation in performance.
Compression of ResNet shows more robust results than ECAPA-TDNN with
lower-bitwidth quantization. Analysis of the layer weights suggests that the
smooth weight distribution of ResNet may be related to its better robustness.
The generalization ability of the quantized model is validated via a
language-mismatched SV task. Furthermore, analysis by information probing
reveals that the quantized models can retain most of the speaker-relevant
knowledge learned by the original models.
