  It is now well-known that automatic speaker verification (ASV) systems can be
spoofed using various types of adversaries. The usual approach to counteract
ASV systems against such attacks is to develop a separate spoofing
countermeasure (CM) module to classify speech input either as a bonafide, or a
spoofed utterance. Nevertheless, such a design requires additional computation
and utilization efforts at the authentication stage. An alternative strategy
involves a single monolithic ASV system designed to handle both zero-effort
imposter (non-targets) and spoofing attacks. Such spoof-aware ASV systems have
the potential to provide stronger protections and more economic computations.
To this end, we propose to generalize the standalone ASV (G-SASV) against
spoofing attacks, where we leverage limited training data from CM to enhance a
simple backend in the embedding space, without the involvement of a separate CM
module during the test (authentication) phase. We propose a novel yet simple
backend classifier based on deep neural networks and conduct the study via
domain adaptation and multi-task integration of spoof embeddings at the
training stage. Experiments are conducted on the ASVspoof 2019 logical access
dataset, where we improve the performance of statistical ASV backends on the
joint (bonafide and spoofed) and spoofed conditions by a maximum of 36.2% and
49.8% in terms of equal error rates, respectively.
