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The nonlinear characteristic of Chinese speech signal is further studied, combined with radical basis function neural network, a nonlinear prediction model is designed. Firstly, delay time, embed dimension and maximum lyapunov exponent of Chinese speech phoneme are calculated by using C-C algorithm, false neatest neighbor algorithm and wolf algorithm, it is found out that Chinese speech signal has nonlinear characteristic. Secondly, combined with delay time and embed dimension, radical basis function neural network analysis method is applied successfully to design nonlinear prediction model. Lastly, compared with adaptive differential pulse code modulation linear prediction model and back propagation neural network nonlinear prediction model, prediction error of the nonlinear prediction model is significantly reduced, and the prediction performance gets much better.

Design of the Nonlinear Prediction Model for Chinese Speech Signal Based on RBF Neural Network