Training algorithm to deceive Anti-Spoofing Verification for DNN-based speech synthesis

@article{Saito2017TrainingAT,
  title={Training algorithm to deceive Anti-Spoofing Verification for DNN-based speech synthesis},
  author={Yuki Saito and Shinnosuke Takamichi and Hiroshi Saruwatari},
  journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2017},
  pages={4900-4904}
}
This paper proposes a novel training algorithm for high-quality Deep Neural Network (DNN)-based speech synthesis. The parameters of synthetic speech tend to be over-smoothed, and this causes significant quality degradation in synthetic speech. The proposed algorithm takes into account an Anti-Spoofing Verification (ASV) as an additional constraint in the acoustic model training. The ASV is a discriminator trained to distinguish natural and synthetic speech. Since acoustic models for speech… CONTINUE READING
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