An experimental study of speaker verification sensitivity to computer voice-altered imposters

This paper investigates the relative sensitivity of a Gaussian mixture model (GMM) based voice verification algorithm to computer voice-altered imposters. First, a new trainable speech synthesis algorithm based on trajectory models of the speech line spectral frequency (LSF) parameters is presented in order to model the spectral characteristics of a target voice. A GMM based speaker verifier is then constructed for the 138 speaker YOHO database and shown to have an initial equal-error rate (EER) of 1.45% for the case of casual imposter attempts using a single combination-lock phrase test. Next, imposter voices are automatically altered using the synthesis algorithm to mimic the customer´s voice. After voice transformation, the false acceptance rate is shown to increase from 1.45% to over 86% if the baseline EER threshold is left unmodified. Furthermore, at a customer false rejection rate of 25%, the false acceptance rate for the voice-altered imposter remains as high as 34.6%