The research and application of speech synthesis in Chinese and English are widely used. However, most nonuniversal languages have relatively few electronic language resources, and speech synthesis research is lagging behind. Burmese is a type of alphabetic writing, and Burmese belongs to Tibetan-Burmese branch of the Sino-Tibetan language. In order to develop the Burmese speech synthesis application system, this paper studies the Burmese speech waveform synthesis method, designs and implements a HMM-based Burmese speech synthesis baseline system, and based on this, introduces a deep neural network (DNN) to replace the decision tree model of HMM speech synthesis system, thereby improving the acoustic model to improve the quality of speech synthesis. The experimental results show that the baseline system is feasible, and the introduction of DNN speech synthesis system can effectively improve the quality of speech synthesis.