  In the Text-to-speech(TTS) task, the latent diffusion model has excellent
fidelity and generalization, but its expensive resource consumption and slow
inference speed have always been a challenging. This paper proposes Discrete
Diffusion Model with Contrastive Learning for Text-to-Speech Generation(DCTTS).
The following contributions are made by DCTTS: 1) The TTS diffusion model based
on discrete space significantly lowers the computational consumption of the
diffusion model and improves sampling speed; 2) The contrastive learning method
based on discrete space is used to enhance the alignment connection between
speech and text and improve sampling quality; and 3) It uses an efficient text
encoder to simplify the model's parameters and increase computational
efficiency. The experimental results demonstrate that the approach proposed in
this paper has outstanding speech synthesis quality and sampling speed while
significantly reducing the resource consumption of diffusion model. The
synthesized samples are available at https://github.com/lawtherWu/DCTTS.
