  The main objective of the spoofing countermeasure system is to detect the
artifacts within the input speech caused by the speech synthesis or voice
conversion process. In order to achieve this, we propose to adopt an attentive
activation function, more specifically attention rectified linear unit (AReLU)
to the end-to-end spoofing countermeasure system. Since the AReLU employs the
attention mechanism to boost the contribution of relevant input features while
suppressing the irrelevant ones, introducing AReLU can help the countermeasure
system to focus on the features related to the artifacts. The proposed
framework was experimented on the logical access (LA) task of ASVSpoof2019
dataset, and outperformed the systems using the standard non-learnable
activation functions.
