  In a biometric system, each biometric sample or template is typically
associated with a single identity. However, recent research has demonstrated
the possibility of generating "morph" biometric samples that can successfully
match more than a single identity. Morph attacks are now recognized as a
potential security threat to biometric systems. However, most morph attacks
have been studied on biometric modalities operating in the image domain, such
as face, fingerprint, and iris. In this preliminary work, we introduce Voice
Identity Morphing (VIM) - a voice-based morph attack that can synthesize speech
samples that impersonate the voice characteristics of a pair of individuals.
Our experiments evaluate the vulnerabilities of two popular speaker recognition
systems, ECAPA-TDNN and x-vector, to VIM, with a success rate (MMPMR) of over
80% at a false match rate of 1% on the Librispeech dataset.
