LP-WaveNet: Linear Prediction-based WaveNet Speech Synthesis

Abstract

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.

arXiv commentSubmitted to EUSIPCO 2020

References in corpus (2)

• Adam: A Method for Stochastic Optimization
• WaveNet: A Generative Model for Raw Audio

Cited by in corpus (6)

• Neural source-filter waveform models for statistical parametric speech synthesis
• TTS-by-TTS: TTS-driven Data Augmentation for Fast and High-Quality Speech Synthesis
• Improved parallel WaveGAN vocoder with perceptually weighted spectrogram loss
• Training a Neural Speech Waveform Model using Spectral Losses of Short-Time Fourier Transform and Continuous Wavelet Transform
• Speaker-adaptive neural vocoders for parametric speech synthesis systems
• Neural text-to-speech with a modeling-by-generation excitation vocoder