  We present our system submission to the ASVspoof 2019 Challenge Physical
Access (PA) task. The objective for this challenge was to develop a
countermeasure that identifies speech audio as either bona fide or intercepted
and replayed. The target prediction was a value indicating that a speech
segment was bona fide (positive values) or "spoofed" (negative values). Our
system used convolutional neural networks (CNNs) and a representation of the
speech audio that combined x-vector attack embeddings with signal processing
features. The x-vector attack embeddings were created from mel-frequency
cepstral coefficients (MFCCs) using a time-delay neural network (TDNN). These
embeddings jointly modeled 27 different environments and 9 types of attacks
from the labeled data. We also used sub-band spectral centroid magnitude
coefficients (SCMCs) as features. We included an additive Gaussian noise layer
during training as a way to augment the data to make our system more robust to
previously unseen attack examples. We report system performance using the
tandem detection cost function (tDCF) and equal error rate (EER). Our approach
performed better that both of the challenge baselines. Our technique suggests
that our x-vector attack embeddings can help regularize the CNN predictions
even when environments or attacks are more challenging.
