  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.
