This paper proposes a source-filter-based generative adversarial neural vocoder named SF-GAN, which achieves high-fidelity waveform generation from input acoustic features by introducing F0-based source excitation signals to a neural filter framework. The SF-GAN vocoder is composed of a source module and a resolution-wise conditional filter module and is trained based on generative adversarial strategies. The source module produces an excitation signal from the F0 information, then the resolution-wise convolutional filter module combines the excitation signal with processed acoustic features at various temporal resolutions and finally reconstructs the raw waveform. The experimental results show that our proposed SF-GAN vocoder outperforms the state-of-the-art HiFi-GAN and Fre-GAN in both analysis-synthesis (AS) and text-to-speech (TTS) tasks, and the synthesized speech quality of SF-GAN is comparable to the ground-truth audio.

翻译：本文提出一种基于源-滤波器的生成对抗神经声码器SF-GAN，通过将基于F0的源激励信号引入神经滤波器框架，实现从输入声学特征生成高保真波形。SF-GAN声码器由源模块和分辨率条件滤波器模块组成，并基于生成对抗策略进行训练。源模块从F0信息生成激励信号，随后分辨率条件卷积滤波器模块将该激励信号与不同时间分辨率下处理的声学特征相结合，最终重建原始波形。实验结果表明，所提出的SF-GAN声码器在分析-合成(AS)和文语转换(TTS)任务中均优于当前最优的HiFi-GAN和Fre-GAN，且其合成语音质量与真实语音相当。