QHARMA-GAN：基于自回归滑动平均模型的准谐波神经声码器
QHARMA-GAN: Quasi-Harmonic Neural Vocoder based on Autoregressive Moving Average Model
AI总结：
本文提出QHARMA-GAN神经声码器，将神经网络与准谐波模型及ARMA函数结合，实现高质量、可灵活修改且更快速的语音合成。
摘要：
声码器将语音信号编码为声学特征并允许从中重建语音信号，已被研究数十年。近年来，深度学习的兴起尤其推动了神经声码器的发展，以生成高质量语音信号。另一方面，现有的端到端神经声码器存在黑箱特性，遮蔽了语音产生机制和语音的内在结构，导致难以分别建模源激励和共振特性，并丧失灵活合成或高质量修改语音的能力。此外，它们逐序列生成波形通常需要复杂网络，导致大量时间消耗。在本工作中，受将语音表示为稀疏分量的准谐波模型（QHM）启发，我们将神经网络与QHM合成过程相结合，提出一种新颖的神经声码器框架。据此，语音信号可被编码为自回归滑动平均（ARMA）函数以建模共振特性，从而在任意频率上准确估计准谐波的幅度和相位。随后，语音可以被重新合成，并在音高移位和时间拉伸方面进行任意修改，同时保持高质量，而时间消耗和网络规模均有所降低。实验表明，所提方法利用了QHM、ARMA模型和神经网络的优点，使我们的方法在生成速度、合成质量和修改灵活性方面优于其他方法。
英文摘要：
Vocoders, encoding speech signals into acoustic features and allowing for speech signal reconstruction from them, have been studied for decades. Recently, the rise of deep learning has particularly driven the development of neural vocoders to generate high-quality speech signals. On the other hand, the existing end-to-end neural vocoders suffer from a black-box nature that blinds the speech production mechanism and the intrinsic structure of speech, resulting in the ambiguity of separately modeling source excitation and resonance characteristics and the loss of flexibly synthesizing or modifying speech with high quality. Moreover, their sequence-wise waveform generation usually requires complicated networks, leading to substantial time consumption. In this work, inspired by the quasi-harmonic model (QHM) that represents speech as sparse components, we combine the neural network and QHM synthesis process to propose a novel framework for the neural vocoder. Accordingly, speech signals can be encoded into autoregressive moving average (ARMA) functions to model the resonance characteristics, yielding accurate estimates of the amplitudes and phases of quasi-harmonics at any frequency. Subsequently, the speech can be resynthesized and arbitrarily modified in terms of pitch shifting and time stretching with high quality, whereas the time consumption and network size decrease. The experiments indicate that the proposed method leverages the strengths of QHM, the ARMA model, and neural networks, leading to the outperformance of our methods over other methods in terms of generation speed, synthesis quality, and modification flexibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
