Nonlinear bi-directional prediction model of speech signals based on genetic programming

Based on the linear prediction theory, This paper proposes a nonlinear bi-directional prediction model for speech signals. After the data preprocessing, an improved GP is used to construct bi-directional prediction model of each frame. Then by the analysis of these models, the normalized nonlinear bi-directional prediction model is obtained. In the experiments, the DUPSO algorithm is used to optimize the parameters. By comparing the SNR and MMSE with the linear prediction model, the superiority of the proposed nonlinear bi-directional prediction model is proved.