Exploiting magnitude and phase spectral information for converted speech detection

Speaker verification systems have been shown to be vulnerable in situations where voice conversion techniques are used to try to fool them, evidencing an important security breach in these applications. This work focuses on the development of a new converted speech detector able to robustly address this problem. The proposed detector uses four spectral features extracted from the magnitude and the phase spectrum of the speech signal. To evaluate the performance of the detector we use a subset of the core task of the NIST SRE2006 corpus as the natural data. The converted data was produced with two different voice conversion methods: Gaussian mixture model and unit selection, from other NIST SRE2006 conditions. The converted speech detector achieved a detection accuracy of 99.1% and 98.5% for natural and converted utterances, respectively.