Evaluation and Promotion of a Multidimensional Information Intelligent Speech System in Dialect Teaching

Abstract

Due to the wide scale of learners, large individual differences and scattered distribution, dialect teaching is difficult to carry out effectively by traditional school education. In order to improve the teaching level of dialect, taking Cantonese as an example, this paper constructs a teaching evaluation system based on multidimensional information. Through the questionnaire and investigation of large Cantonese training institutions in Guangdong Province, the data set is formed, the CMA-ES algorithm with efficient optimization ability is selected to optimize the SVM, and the model is compared with ACO and SVM without optimization algorithm. Experimental results show that the average accuracy of CMA-ES algorithm is 95.85% and the average running time is 21.0 ms on 8 data sets, which has obvious advantages relatively. Based on the evaluation model, the basis for teaching optimization is found through sensitivity analysis, and student’s language expression is the most important index. And with the help of the intelligent voice system, the improvement measures for Cantonese teaching are proposed from the aspects of scene, oral, and scoring.