  This study aims to develop a single integrated spoofing-aware speaker
verification (SASV) embeddings that satisfy two aspects. First, rejecting
non-target speakers' input as well as target speakers' spoofed inputs should be
addressed. Second, competitive performance should be demonstrated compared to
the fusion of automatic speaker verification (ASV) and countermeasure (CM)
embeddings, which outperformed single embedding solutions by a large margin in
the SASV2022 challenge. We analyze that the inferior performance of single SASV
embeddings comes from insufficient amount of training data and distinct nature
of ASV and CM tasks. To this end, we propose a novel framework that includes
multi-stage training and a combination of loss functions. Copy synthesis,
combined with several vocoders, is also exploited to address the lack of
spoofed data. Experimental results show dramatic improvements, achieving a
SASV-EER of 1.06% on the evaluation protocol of the SASV2022 challenge.
