  This paper describes our proposed integration system for the spoofing-aware
speaker verification challenge. It consists of a robust spoofing-aware
verification system that use the speaker verification and antispoofing
embeddings extracted from specialized neural networks. First, an integration
network, fed with the test utterance's speaker verification and spoofing
embeddings, is used to compute a spoof-based score. This score is then linearly
combined with the cosine similarity between the speaker verification embeddings
from the enrollment and test utterances, thus obtaining the final scoring
decision. Moreover, the integration network is trained using a one-class loss
function to discriminate between target trials and unauthorized accesses. Our
proposed system is evaluated in the ASVspoof19 database, exhibiting competitive
performance compared to other integration approaches. In addition, we test,
along with our integration approach, state of the art speaker verification and
antispoofing systems based on self-supervised learning, yielding
high-performance speech biometric systems.
