Title: Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder,
Abstract: Neural networks based vocoders, typically the WaveNet, have achieved
spectacular performance for text-to-speech (TTS) in recent years. 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 distilled training
framework, which makes it tedious to adapt a well trained model to a new
speaker. To address these two problems, this paper proposes 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. Our
subjective experiments shows that the proposed training method can further
reduce the quality gap between generated and natural waveforms.