Recent development of neural vocoders based on the generative adversarial neural network (GAN) has shown obvious advantages of generating raw waveform conditioned on mel-spectrogram with fast inference speed and lightweight networks. Whereas, it is still challenging to train a universal neural vocoder that can synthesize high-fidelity speech from various scenarios with unseen speakers, languages, and speaking styles. In this paper, we propose DSPGAN, a GAN-based universal vocoder for high-fidelity speech synthesis by applying the time-frequency domain supervision from digital signal processing (DSP). To eliminate the mismatch problem caused by the ground-truth spectrograms in the training phase and the predicted spectrograms in the inference phase, we leverage the mel-spectrogram extracted from the waveform generated by a DSP module, rather than the predicted mel-spectrogram from the Text-to-Speech (TTS) acoustic model, as the time-frequency domain supervision to the GAN-based vocoder. We also utilize sine excitation as the time-domain supervision to improve the harmonic modeling and eliminate various artifacts of the GAN-based vocoder. Experiments show that DSPGAN significantly outperforms the compared approaches and it can generate high-fidelity speech for various TTS models trained using diverse data.

翻译：基于生成对抗神经网络（GAN）的神经声码器近年发展表明，其在以梅尔频谱图为条件生成原始波形时具有推理速度快、网络轻量级的显著优势。然而，训练一个能够针对未见说话人、语言和说话风格的多场景合成高保真语音的通用神经声码器仍具挑战性。本文提出DSPGAN——一种通过数字信号处理（DSP）时频域监督实现高保真语音合成的GAN通用声码器。为解决训练阶段真实频谱图与推理阶段预测频谱图之间的失配问题，我们利用DSP模块生成波形中提取的梅尔频谱图（而非文本转语音声学模型预测的梅尔频谱图）作为GAN声码器的时频域监督。同时采用正弦激励作为时域监督以增强谐波建模并消除GAN声码器的各类伪影。实验表明，DSPGAN显著优于对比方法，能为使用多样化数据训练的不同语音合成模型生成高保真语音。