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

Abstract

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%.