  We propose a linear prediction (LP)-based waveform generation method via
WaveNet vocoding framework. A WaveNet-based neural vocoder has significantly
improved the quality of parametric text-to-speech (TTS) systems. However, it is
challenging to effectively train the neural vocoder when the target database
contains massive amount of acoustical information such as prosody, style or
expressiveness. As a solution, the approaches that only generate the vocal
source component by a neural vocoder have been proposed. However, they tend to
generate synthetic noise because the vocal source component is independently
handled without considering the entire speech production process; where it is
inevitable to come up with a mismatch between vocal source and vocal tract
filter. To address this problem, we propose an LP-WaveNet vocoder, where the
complicated interactions between vocal source and vocal tract components are
jointly trained within a mixture density network-based WaveNet model. The
experimental results verify that the proposed system outperforms the
conventional WaveNet vocoders both objectively and subjectively. In particular,
the proposed method achieves 4.47 MOS within the TTS framework.
