Research on language simulation and speech recognition based on data simulation of Machine Learning System

In recent years, human-computer interaction project has become a mainstream research topic in the field of artificial intelligence. Among them, the most important interaction mode is voice interaction mode, which is based on voice recognition technology and is also the main factor to promote the development of artificial intelligence technology itself. In addition, speech recognition and modeling in noisy background have also been fully developed. In the actual communication environment, in addition to the voice information of the communicator, there are also noisy background sounds, which will reduce the accuracy of speech recognition. Therefore, it is necessary to conduct modeling research on this problem to improve the recognition ability of the speech recognition model. In this context, this paper combines machine learning technology and artificial intelligence technology to conduct in-depth research to improve the performance of speech recognition model, achieve the balance between efficiency and performance, and run stably and easily. The system is mainly divided into user registration, data flow input and processing, identification model and output results and other modules. The experimental data show that the response time of the system is proportional to the number of test samples. Therefore, as long as resources are allocated reasonably, stable system response time can be obtained, and system performance and scalability can fully meet the requirements of language simulation tasks. In this paper, the optimization of language simulation system is completed through the comprehensive study of machine learning and artificial intelligence technology.