The diffusion models including Denoising Diffusion Probabilistic Models (DDPM) and score-based generative models have demonstrated excellent performance in speech synthesis tasks. However, its effectiveness comes at the cost of numerous sampling steps, resulting in prolonged sampling time required to synthesize high-quality speech. This drawback hinders its practical applicability in real-world scenarios. In this paper, we introduce ReFlow-TTS, a novel rectified flow based method for speech synthesis with high-fidelity. Specifically, our ReFlow-TTS is simply an Ordinary Differential Equation (ODE) model that transports Gaussian distribution to the ground-truth Mel-spectrogram distribution by straight line paths as much as possible. Furthermore, our proposed approach enables high-quality speech synthesis with a single sampling step and eliminates the need for training a teacher model. Our experiments on LJSpeech Dataset show that our ReFlow-TTS method achieves the best performance compared with other diffusion based models. And the ReFlow-TTS with one step sampling achieves competitive performance compared with existing one-step TTS models.

翻译：扩散模型，包括去噪扩散概率模型（DDPM）和基于得分的生成模型，已在语音合成任务中展现出优异的性能。然而，其有效性是以大量采样步骤为代价的，导致合成高质量语音所需的采样时间较长。这一缺陷阻碍了其在实际场景中的应用。在本文中，我们介绍了ReFlow-TTS，一种基于纠正流（rectified flow）的高保真语音合成新方法。具体而言，我们的ReFlow-TTS是一种简单的常微分方程（ODE）模型，它通过尽可能笔直的路径将高斯分布传输到真实梅尔频谱图分布。此外，我们提出的方法能够通过单次采样步骤实现高质量语音合成，并且无需训练教师模型。我们在LJSpeech数据集上的实验表明，与其他基于扩散的模型相比，我们的ReFlow-TTS方法取得了最佳性能。并且，具有一步采样的ReFlow-TTS与现有的一步式TTS模型相比，达到了有竞争力的性能。