Transfer Function-Based Voice Transformation for Speaker Recognition

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 is based on the following idea: in several applications and particularly in forensic situations, it is reasonable to think that some organizations have a knowledge on the speaker recognition method used and could impersonate a given, well known speaker. We also evaluate the effect of the voice transformation when the transformation is applied both on client and impostor trials. This paper presents some experiments based on NIST SRE 2005 protocol. The results show that the voice transformation allows a drastic increase of the false acceptance rate, without damaging the natural aspect of the voice. It seems also that this kind of voice transformation could be efficient for reducing the inter-session mismatch