No abstract

For the problem of the low accuracy of large-scale oral English, based on oral English from the perspective of difficulty of oral English text and phonology, a multimodal-based automatic oral English assessment method is proposed by using L2 regularized (multilayer perceptron, MLP) and 9 features affecting the automated assessment of oral English as model input. Simulation results show that the proposed method can well predictively assess oral English difficulty and performs well on RMSE and R2 metrics of 0.053 and 0.905, respectively, with certain advantages over conventional elastic network-based and random forest-based prediction models.