Abstract: Vietnamese is the official language of the Socialist Republic of Vietnam.It belongs to the Vietnamese branch of the Viet Muang language family of the South Asian language family.In recent years,deep learning-based speech synthesis has been able to synthesize high-quality speech.However,these methods often rely on large-scale high-quality speech training data.An effective way to solve the problem of insufficient data for some low-resource non-lingua franca speech training is to adopt a transfer learning method and borrow other high-resource lingua franca speech data.Under the condition of low resources,with the goal of improving the quality of Vietnamese speech synthesis,the end-to-end speech synthesis model Tacotorn2 is selected as the baseline model,and the effects of different source languages,different text character embedding methods and transfer learning methods on the effect of speech synthesis are studied by transfer learning methods.Then,from both subjective and objective aspects,the speech synthesized by the various models described in this paper is evaluated.Experimental results show that the transfer learning system based on English phonetic module embedding＋Vietnamese phonology embedding method has achieved good results in synthesizing naturally understandable Vietnamese speech,and the MOS score of synthetic speech can reach 4.11,which is much higher than the 2.53 of the baseline system.

Key words: Vietnamese, Speech synthesis, Transfer learning, Text embedding, End-to-end