Detection of synthetic speech for the problem of imposture

In this paper, we present new results from our research into the vulnerability of a speaker verification (SV) system to synthetic speech. We use a HMM-based speech synthesizer, which creates synthetic speech for a targeted speaker through adaptation of a background model and both GMM-UBM and support vector machine (SVM) SV systems. Using 283 speakers from the Wall-Street Journal (WSJ) corpus, our SV systems have a 0.35% EER. When the systems are tested with synthetic speech generated from speaker models derived from the WSJ journal corpus, over 91% of the matched claims are accepted. We propose the use of relative phase shift (RPS) in order to detect synthetic speech and develop a GMM-based synthetic speech classifier (SSC). Using the SSC, we are able to correctly classify human speech in 95% of tests and synthetic speech in 88% of tests thus significantly reducing the vulnerability.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Phillip L. De Leon, Inma Hernáez, Ibon Saratxaga, Michael Pucher, Junichi Yamagishi
• Keywords: HMM-based Speech Synthesizer, ICASSP 2011, Signal Processing, SV Systems, Synthetic Speech