  Automatic speaker verification (ASV) systems are highly vulnerable to
presentation attacks, also called spoofing attacks. Replay is among the
simplest attacks to mount - yet difficult to detect reliably. The
generalization failure of spoofing countermeasures (CMs) has driven the
community to study various alternative deep learning CMs. The majority of them
are supervised approaches that learn a human-spoof discriminator. In this
paper, we advocate a different, deep generative approach that leverages from
powerful unsupervised manifold learning in classification. The potential
benefits include the possibility to sample new data, and to obtain insights to
the latent features of genuine and spoofed speech. To this end, we propose to
use variational autoencoders (VAEs) as an alternative backend for replay attack
detection, via three alternative models that differ in their
class-conditioning. The first one, similar to the use of Gaussian mixture
models (GMMs) in spoof detection, is to train independently two VAEs - one for
each class. The second one is to train a single conditional model (C-VAE) by
injecting a one-hot class label vector to the encoder and decoder networks. Our
final proposal integrates an auxiliary classifier to guide the learning of the
latent space. Our experimental results using constant-Q cepstral coefficient
(CQCC) features on the ASVspoof 2017 and 2019 physical access subtask datasets
indicate that the C-VAE offers substantial improvement in comparison to
training two separate VAEs for each class. On the 2019 dataset, the C-VAE
outperforms the VAE and the baseline GMM by an absolute 9 - 10% in both equal
error rate (EER) and tandem detection cost function (t-DCF) metrics. Finally,
we propose VAE residuals - the absolute difference of the original input and
the reconstruction as features for spoofing detection.
