LP-WaveNet: Linear Prediction-based WaveNet Speech Synthesis
This work addresses noise issues in speech synthesis for TTS systems, representing an incremental improvement over existing neural vocoder methods.
The paper tackles the challenge of training neural vocoders for high-quality speech synthesis when databases contain complex acoustical information, by proposing LP-WaveNet, which jointly models vocal source and vocal tract interactions, achieving a 4.47 MOS in TTS evaluations.
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.