System assurance guided artificial intelligence vocal training system considering speech spectrum visualization

In this research, a system-guided artificial intelligence vocal training system is designed and further simulated taking into account the speech spectrum visualization. Although the coefficients and cepstrum coefficients can then generally better represent the spectral envelope, at low frequencies, the spectral envelope is often not accurately expressed. Therefore, this paper adopts the information visualization and data analysis models to complete the computer-aided system. Two major contributions are reflected in the paper. First, we use past sample values to predict future sample values, and determine the set of prediction parameters by the error between the actual speech sample and the linear prediction, the statistical model is determined. Second, the system is designed and implemented using coding theory, we construct the robust and efficient vocal guidance system. The experiment compared with the recently published paper has shown the performance of our model. For the designed system, the visualization performance is validated by the massive test and the robustness is higher, which will be meaningful for the complex scenarios.