Personalized recommendation based on improved speech recognition algorithm in music e-learning course simulation

Abstract

In recent years, the rapid development of internet technology has promoted the integration of music performance training systems with new teaching environments. However, personalized recommendations related to users are still a drawback of traditional performance training. Therefore, this article optimizes the system based on the shortcomings of traditional performance training systems and adds algorithms related to speech recognition and personalized recommendations to the music performance training system. Traditional music performance systems still have a lot of problems, resulting in a narrow application prospect. Therefore, this article first improves the speech recognition algorithm, and then uses the advantages of personalized recommendation algorithms in filtering applications to reconstruct the music performance training system. This article has conducted a large number of testing experiments on the system, and the experimental results strongly prove that the optimized speech recognition algorithm is superior to the algorithms used in traditional performance training systems. The model of this research not only incorporates personalized recommendations into the system, but also undergoes multiple optimizations in the experiment, ultimately obtaining a satisfactory product. The system can provide a suitable training platform for music performance, Effectively changing the playing environment and making music performance training more scientific and digital.