  A method for statistical parametric speech synthesis incorporating generative
adversarial networks (GANs) is proposed. Although powerful deep neural networks
(DNNs) techniques can be applied to artificially synthesize speech waveform,
the synthetic speech quality is low compared with that of natural speech. One
of the issues causing the quality degradation is an over-smoothing effect often
observed in the generated speech parameters. A GAN introduced in this paper
consists of two neural networks: a discriminator to distinguish natural and
generated samples, and a generator to deceive the discriminator. In the
proposed framework incorporating the GANs, the discriminator is trained to
distinguish natural and generated speech parameters, while the acoustic models
are trained to minimize the weighted sum of the conventional minimum generation
loss and an adversarial loss for deceiving the discriminator. Since the
objective of the GANs is to minimize the divergence (i.e., distribution
difference) between the natural and generated speech parameters, the proposed
method effectively alleviates the over-smoothing effect on the generated speech
parameters. We evaluated the effectiveness for text-to-speech and voice
conversion, and found that the proposed method can generate more natural
spectral parameters and $F_0$ than conventional minimum generation error
training algorithm regardless its hyper-parameter settings. Furthermore, we
investigated the effect of the divergence of various GANs, and found that a
Wasserstein GAN minimizing the Earth-Mover's distance works the best in terms
of improving synthetic speech quality.
