Although diffusion models in text-to-speech have become a popular choice due to their strong generative ability, the intrinsic complexity of sampling from diffusion models harms their efficiency. Alternatively, we propose VoiceFlow, an acoustic model that utilizes a rectified flow matching algorithm to achieve high synthesis quality with a limited number of sampling steps. VoiceFlow formulates the process of generating mel-spectrograms into an ordinary differential equation conditional on text inputs, whose vector field is then estimated. The rectified flow technique then effectively straightens its sampling trajectory for efficient synthesis. Subjective and objective evaluations on both single and multi-speaker corpora showed the superior synthesis quality of VoiceFlow compared to the diffusion counterpart. Ablation studies further verified the validity of the rectified flow technique in VoiceFlow.

翻译：尽管扩散模型因其强大的生成能力在文本到语音领域成为热门选择，但其采样的内在复杂性降低了效率。为此，我们提出VoiceFlow声学模型，该模型采用整流流匹配算法，以有限的采样步数实现高质量合成。VoiceFlow将生成梅尔频谱图的过程建模为以文本输入为条件的常微分方程，随后估计其向量场。整流流技术有效拉直了采样轨迹，从而实现高效合成。在单说话人与多说话人语料库上的主观与客观评估表明，相较于扩散模型，VoiceFlow的合成质量更优。消融实验进一步验证了整流流技术在VoiceFlow中的有效性。