Optimal design of human-computer interaction system of dialogue robot based on deep reinforcement learning
In order to improve the accuracy of human-robot interaction,a speech enhancement method based on cooperative re-cursive network is proposed to optimize the speech analysis module.Methods Firstly,the AR parameter estimation model of speech signal is constructed based on generalized minimum absolute deviation method,and the model is solved by deep recursive Q network.Then,according to the parameters,the speech signal data is successively restored through the recursive network of Kalman filter.The experiments show that the proposed method can better restore the speech signal in speech enhancement test,especially in short-sight-ed speech denoising,reduce the speech distortion and greatly improve the signal-to-noise ratio of speech,compared with the conven-tional speech enhancement methods such as improved spectral subtraction,YW estimation adaptive Kalman filter and MG adaptive Kalman filter.In the human-computer interaction test,the human-computer interaction system optimized based on the proposed speech enhancement method has a dialogue recognition accuracy of 93.33％,which is 16.66％higher than that of the non-optimized system,showing obvious performance advantages and better meeting the human-computer interaction requirements of dialogue robots.
万方数据
维普
