Quasi-Periodic WaveNet Vocoder: A Pitch Dependent Dilated Convolution Model for Parametric Speech Generation

Understand

In this paper, we propose a quasi-periodic neural network (QPNet) vocoder with a novel network architecture named pitch-dependent dilated convolution (PDCNN) to improve the pitch controllability of WaveNet (WN) vocoder.

• The effectiveness of the WN vocoder to generate high-fidelity speech samples from given acoustic features has been proved recently.
• However, because of the fixed dilated convolution and generic network architecture, the WN vocoder hardly generates speech with given F0 values which are outside the range observed in training data.
• Consequently, the WN vocoder lacks the pitch controllability which is one of the essential capabilities of conventional vocoders.