  This paper proposes an effective probability density distillation (PDD)
algorithm for WaveNet-based parallel waveform generation (PWG) systems.
Recently proposed teacher-student frameworks in the PWG system have
successfully achieved a real-time generation of speech signals. However, the
difficulties optimizing the PDD criteria without auxiliary losses result in
quality degradation of synthesized speech. To generate more natural speech
signals within the teacher-student framework, we propose a novel optimization
criterion based on generative adversarial networks (GANs). In the proposed
method, the inverse autoregressive flow-based student model is incorporated as
a generator in the GAN framework, and jointly optimized by the PDD mechanism
with the proposed adversarial learning method. As this process encourages the
student to model the distribution of realistic speech waveform, the perceptual
quality of the synthesized speech becomes much more natural. Our experimental
results verify that the PWG systems with the proposed method outperform both
those using conventional approaches, and also autoregressive generation systems
with a well-trained teacher WaveNet.
