Voice-Clone Attribution Fails for Professional Voice Actors Due to Embedding Geometry Limits
A study of 1,168 Japanese voice actors demonstrates that AI voice-clone attribution systems based on embedding similarity have a persistent misidentification floor of approximately 2.6%, even when using advanced ensemble methods. Under session-disjoint conditions, this error rate rises to 13%. Generic encoders cause about half of clones from non-enrolled individuals to be wrongly attributed to enrolled actors, while 32% of clones of enrolled targets are missed. Domain-matched encoders reduce, but do not eliminate, these attribution errors, revealing that fixed-threshold attribution remains unreliable and potentially unfair.
Why it matters: This research highlights a fundamental limitation in current voice-clone attribution systems, raising concerns about the protection and fair treatment of professional voice actors.
Full story at: arXiv Audio and Speech Processing ↗