2026-10-14T15:20:44Z https://www.scidb.cn/oai

10.57760/sciencedb.35286 2026-05-06T21:41:11Z

2026-05-06 Voice Conversion Spoofing Dataset for Source Speaker Verification doi:10.57760/sciencedb.35286 en With the rapid development of deep learning-based Voice Conversion (VC) technology, attackers can compromise the security of Automatic Speaker Verification (ASV) systems by tampering with the source speaker's speech signal to mimic the target speaker's acoustic characteristics. The existing anti-spoofing countermeasures primarily focus on distinguishing between genuine and spoofed speech, yet underutilize the implicit source speaker identity information concealed within the spoofed speech. This limitation hinders their ability to meet practical demands in scenarios such as forensic investigation and fraud traceability. Currently, research on source speaker identity verification and discrimination remains insufficient, and existing methods suffer from limited generalisation capabilities. To address this, this paper constructs a new voice conversion spoofing dataset specifically designed for source speaker verification, named VCSD-SSV (voice conversion spoofing dataset for source speaker verification). This dataset encompasses six VC algorithms (i.e., Seed-VC, FACodec, DDDM-VC, Diff-HierVC, TriAAN-VC, and Free-VC) and includes 400 speakers, totaling 327 677 speech samples (46 811 genuine samples and 280 866 converted samples).  Voice Conversion; Speaker Verification; Spoofing Dataset Su Zhaopin EMBARGO https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank