  In this paper, we propose an improved LPCNet vocoder using a linear
prediction (LP)-structured mixture density network (MDN). The recently proposed
LPCNet vocoder has successfully achieved high-quality and lightweight speech
synthesis systems by combining a vocal tract LP filter with a WaveRNN-based
vocal source (i.e., excitation) generator. However, the quality of synthesized
speech is often unstable because the vocal source component is insufficiently
represented by the mu-law quantization method, and the model is trained without
considering the entire speech production mechanism. To address this problem, we
first introduce LP-MDN, which enables the autoregressive neural vocoder to
structurally represent the interactions between the vocal tract and vocal
source components. Then, we propose to incorporate the LP-MDN to the LPCNet
vocoder by replacing the conventional discretized output with continuous
density distribution. The experimental results verify that the proposed system
provides high quality synthetic speech by achieving a mean opinion score of
4.41 within a text-to-speech framework.
