Research on an auxiliary voice training system based on improved particle swarm optimization algorithm and support vector
With the advancement of technology,especially in the field of artificial intelligence,global communication is becoming increasingly close,making human-computer interaction technology across languages an important part of life.In order to solve the problem of English pronunciation and its related equipment control problems,an auxiliary pronunciation training system combining op-timized particle swarm optimization algorithm and support vector regression is developed.Firstly,the system innovates particle swarm optimization algorithm and integrates chaos mechanism.This mechanism is then combined with the support vector regression algo-rithm.The results show that compared with the traditional method,the new combination strategy greatly reduces the computation times and cost.The average computation times of the standard particle swarm algorithm is 23 times,the chaotic version is 17 times,and the genetic version is 13 times,and the proposed method only needs 7 times.It can be seen that this new method has improved the search ability and accuracy,and has brought great value for practical application.
万方数据
维普
