Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder

Understand

Although state-of-the-art parallel WaveNet has addressed the issue of real-time waveform generation, there remains problems.

• Firstly, due to the noisy input signal of the model, there is still a gap between the quality of generated and natural waveforms.
• Secondly, a parallel WaveNet is trained under a distillation framework, which makes it tedious to adapt a well trained model to a new speaker.
• To address these two problems, in this paper we propose an end-to-end adaptation method based on the generative adversarial network (GAN), which can reduce the computational cost for the training of new speaker adaptation.