  Automatic speaker verification systems are vulnerable to a variety of access
threats, prompting research into the formulation of effective spoofing
detection systems to act as a gate to filter out such spoofing attacks. This
study introduces a simple attention module to infer 3-dim attention weights for
the feature map in a convolutional layer, which then optimizes an energy
function to determine each neuron's importance. With the advancement of both
voice conversion and speech synthesis technologies, unseen spoofing attacks are
constantly emerging to limit spoofing detection system performance. Here, we
propose a joint optimization approach based on the weighted additive angular
margin loss for binary classification, with a meta-learning training framework
to develop an efficient system that is robust to a wide range of spoofing
attacks for model generalization enhancement. As a result, when compared to
current state-of-the-art systems, our proposed approach delivers a competitive
result with a pooled EER of 0.99% and min t-DCF of 0.0289.
