Simulation of Chinese Speech Synthesis System Based on Wavelet Optimized Neural Network

Language is the most important communication tool for human beings, because it is natural, convenient, accurate and efficient. With the popularization and application of speech synthesis technology, higher requirements are put forward for the quality of speech synthesis. Speech recognition and speech synthesis technology has become a symbol of the development of modern technology, and it is also an important field of research and development of modern computer technology. Wavelet neural network is a "new neural network model" constructed by combining wavelet transform theory with artificial neural network, which combines the good time-frequency localization property of wavelet transform and the self-learning function of neural network. The former mainstream end-to-end speech synthesis technology of deep neural network has been able to generate audio files similar to real human speech for languages based on Latin characters, such as English. However, Chinese is different from this kind of language. Chinese is a language written continuously, and the correlation between Chinese characters and their pronunciations is low. When the current mainstream deep neural network speech synthesis method is applied to Chinese, there are serious prosodic problems in the synthesized audio, such as inappropriate pauses, broken sentences, mispronunciations of polyphonic words and poor naturalness. In this paper, a wavelet neural network structure is constructed, and the gradient method is used for network training, and good speech recognition results are obtained.