Artificial impostor voice transformation effects on false acceptance rates

Abstract

This paper investigates the effect of a transfer function-based voice transformation on automatic speaker recognition system performance. We focus on increasing the impostor acceptance rate, by modifying the voice of an impostor in order to target a specific speaker. This paper follows previous works where we demonstrate that, if someone has a knowledge on the speaker recognition method used, it is possible to impersonate a given speaker, in the view of this speaker recognition method. In this paper we extend the previous work by relaxing the needed knowledge on the targeted speaker recognition system. The results show that the voice transformation allows a drastic increase of the false acceptance rate, without damaging the natural perception of the voice, and without needing a large knowledge on the targeted speaker recognition system.