Parallel waveform synthesis based on generative adversarial networks with voicing-aware conditional discriminators

2020/10/27 by Ryuichi Yamamoto, Yamamoto, Ryuichi, Eunwoo Song +5 · 1 citation
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Music and Audio Processing #Signal Processing (eess.SP) #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2010.14151

openalex publication_date 2020/10/27 · openalex created_date 2020/11/09 · openalex updated_date 2026/07/28

Abstract

This paper proposes voicing-aware conditional discriminators for Parallel WaveGAN-based waveform synthesis systems. In this framework, we adopt a projection-based conditioning method that can significantly improve the discriminator's performance. Furthermore, the conventional discriminator is separated into two waveform discriminators for modeling voiced and unvoiced speech. As each discriminator learns the distinctive characteristics of the harmonic and noise components, respectively, the adversarial training process becomes more efficient, allowing the generator to produce more realistic speech waveforms. Subjective test results demonstrate the superiority of the proposed method over the conventional Parallel WaveGAN and WaveNet systems. In particular, our speaker-independently trained model within a FastSpeech 2 based text-to-speech framework achieves the mean opinion scores of 4.20, 4.18, 4.21, and 4.31 for four Japanese speakers, respectively.

Citations

• MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

Cited by

• Learning to Highlight Audio by Watching Movies

Related

• Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram
• Wave-U-Net Discriminator: Fast and Lightweight Discriminator for Generative Adversarial Network-Based Speech Synthesis
• Waveform generation for text-to-speech synthesis using pitch-synchronous\n multi-scale generative adversarial networks
• Probability density distillation with generative adversarial networks for high-quality parallel waveform generation
• Parallel WaveNet: Fast High-Fidelity Speech Synthesis