  This paper proposes a deep multi-speaker text-to-speech (TTS) model for
spoofing speaker verification (SV) systems. The proposed model employs one
network to synthesize time-downsampled mel-spectrograms from text input and
another network to convert them to linear-frequency spectrograms, which are
further converted to the time domain using the Griffin-Lim algorithm. Both
networks are trained separately under the generative adversarial networks (GAN)
framework. Spoofing experiments on two state-of-the-art SV systems (i-vectors
and Google's GE2E) show that the proposed system can successfully spoof these
systems with a high success rate. Spoofing experiments on anti-spoofing systems
(i.e., binary classifiers for discriminating real and synthetic speech) also
show a high spoof success rate when such anti-spoofing systems' structures are
exposed to the proposed TTS system.
