ACA-Net: Towards Lightweight Speaker Verification using Asymmetric Cross
  Attention
Jia Qi Yip Tuan Truong Dianwen Ng Chong Zhang Yukun Ma Trung Hieu Nguyen Chongjia Ni Shengkui Zhao Eng Siong Chng Bin Ma
In this paper, we propose ACA-Net, a lightweight, global context-aware
speaker embedding extractor for Speaker Verification (SV) that improves upon
existing work by using Asymmetric Cross Attention (ACA) to replace temporal
pooling. ACA is able to distill large, variable-length sequences into small,
fixed-sized latents by attending a small query to large key and value matrices.
In ACA-Net, we build a Multi-Layer Aggregation (MLA) block using ACA to
generate fixed-sized identity vectors from variable-length inputs. Through
global attention, ACA-Net acts as an efficient global feature extractor that
adapts to temporal variability unlike existing SV models that apply a fixed
function for pooling over the temporal dimension which may obscure information
about the signal's non-stationary temporal variability. Our experiments on the
WSJ0-1talker show ACA-Net outperforms a strong baseline by 5\% relative
improvement in EER using only 1/5 of the parameters.