Abstract: With the development of deep learning technology and the progression of speech synthesis research,synthetic speech in widely spoken and high-resource languages such as Chinese and English has increasingly approached natural speech.Vietnamese,a tonal language closely related to Chinese,belongs to the Vietic branch of the Austroasiatic language family of South Asian languages.Due to the scale of available corpus data and the depth of related research,Vietnamese speech synthesis is still significantly short of natural speech.At the premise of low resources,two methods are proposed to improve the naturalness of Vietnamese speech synthesis:1)The phoneme encoder is constructed based on pre-trained phoneme large language model XPhoneBERT,which significantly improves the prosodic expressiveness of Vietnamese speech synthesis with limited data set.2)Improve the U-Net structure in the lightweight diffusion TTS model LightGrad,add nested jump paths,so that the model can be fully trained under low resource conditions,capture more effective information,improve the accuracy of noise prediction,and thus improve the quality of speech synthesis.Experiment results show that the objective and subjective evaluation performance of the Vietnamese speech synthesis system has been significantly improved by using the proposed method.MCD and MOS are up to 6.25 and 4.22 respectively,which are significantly decreased and increased respectively,compared with 7.44 and 3.56 of the baseline system.

Key words: Speech synthesis, Vietnamese, Low resources, Large language model, Diffusion model