This paper proposes ESTVocoder, a novel excitation-spectral-transformed neural vocoder within the framework of source-filter theory. The ESTVocoder transforms the amplitude and phase spectra of the excitation into the corresponding speech amplitude and phase spectra using a neural filter whose backbone is ConvNeXt v2 blocks. Finally, the speech waveform is reconstructed through the inverse short-time Fourier transform (ISTFT). The excitation is constructed based on the F0: for voiced segments, it contains full harmonic information, while for unvoiced segments, it is represented by noise. The excitation provides the filter with prior knowledge of the amplitude and phase patterns, expecting to reduce the modeling difficulty compared to conventional neural vocoders. To ensure the fidelity of the synthesized speech, an adversarial training strategy is applied to ESTVocoder with multi-scale and multi-resolution discriminators. Analysis-synthesis and text-to-speech experiments both confirm that our proposed ESTVocoder outperforms or is comparable to other baseline neural vocoders, e.g., HiFi-GAN, SiFi-GAN, and Vocos, in terms of synthesized speech quality, with a reasonable model complexity and generation speed. Additional analysis experiments also demonstrate that the introduced excitation effectively accelerates the model's convergence process, thanks to the speech spectral prior information contained in the excitation.

翻译：本文提出ESTVocoder，一种在源-滤波器理论框架下的新型激励-频谱变换神经声码器。该模型通过以ConvNeXt v2块为骨干的神经滤波器，将激励信号的幅度谱与相位谱转换为对应语音的幅度谱与相位谱，最终通过逆短时傅里叶变换（ISTFT）重建语音波形。激励信号基于基频F0构建：有声段包含完整谐波信息，无声段则由噪声表示。该激励为滤波器提供了幅度与相位模式的先验知识，相较于传统神经声码器有望降低建模难度。为确保合成语音的保真度，ESTVocoder采用对抗训练策略，配备多尺度与多分辨率判别器。分析-合成实验与文本-语音转换实验均证实，所提出的ESTVocoder在合成语音质量上优于或媲美其他基线神经声码器（如HiFi-GAN、SiFi-GAN与Vocos），且具有合理的模型复杂度与生成速度。补充分析实验进一步表明，得益于激励信号所蕴含的语音频谱先验信息，所引入的激励机制有效加速了模型的收敛过程。