  Voice conversion systems can transform audio to mimic another speaker's
voice, thereby attacking speaker verification systems. However, ongoing studies
on source speaker verification are hindered by limited data availability and
methodological constraints. In this paper, we generate a large-scale converted
speech database and train a batch of baseline systems based on the
MFA-Conformer architecture to promote the source speaker verification task. In
addition, we introduce a related task called conversion method recognition. An
adapter-based multi-task learning approach is employed to achieve effective
conversion method recognition without compromising source speaker verification
performance. Additionally, we investigate and effectively address the open-set
conversion method recognition problem through the implementation of an open-set
nearest neighbor approach.
