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.
In recent years, the multi-dimensional visualization technology continues to accelerate, resulting in a huge amount of data, higher requirements for related technologies, and more opportunities. Under the background of big data technology application, multi-dimensional visualization methods can shine in different elds, such as applying them to linguistic research. Although the technology has a wide range of applications, it can not effectively and intuitively display multi-dimensional voice feature data, which is di cult to fully meet the requirements of parameter visualization. In order to deeply study linguistic speech recognition and other issues, this paper introduces speech recognition technology to complete the creation and improvement of a multi-dimensional perspective analysis system for speech data, and uses socket mechanism to complete the server construction, including voice recognition, data enhancement, model training and other modules. This system takes the target voice data collection as the calling end, Thus, based on socket connection, data interaction with the server can meet the task requirements of the multi-dimensional perspective analysis system, and can achieve two-way data interaction. The simulation experiment results show that the system based on OpenSMILE toolbox can effectively obtain high-dimensional features, and its performance is excellent, which can meet most of the task requirements. It contains many kinds of acoustic feature data, which can solve the problem of over compression of the original signal, and mine the characteristics of voice waves to explain the relationship between frames. The system is higher than low dimensional features in recognizing multiple speakers. This paper designs an effective simulation system by applying speech recognition technology to multidimensional linguistic data analysis in the context of big data.
In recent years, the multi-dimensional visualization technology continues to accelerate, resulting in a huge amount of data, higher requirements for related technologies, and more opportunities. Under the background of big data technology application, multi-dimensional visualization methods can shine in different fields, such as applying them to linguistic research. Although the technology has a wide range of applications, it can not effectively and intuitively display multi-dimensional voice feature data, which is difficult to fully meet the requirements of parameter visualization. In order to deeply study linguistic speech recognition and other issues, this paper introduces speech recognition technology to complete the creation and improvement of a multi-dimensional perspective analysis system for speech data, and uses socket mechanism to complete the server construction, including voice recognition, data enhancement, model training and other modules. This system takes the target voice data collection as the calling end, Thus, based on socket connection, data interaction with the server can meet the task requirements of the multi-dimensional perspective analysis system, and can achieve two-way data interaction. The simulation experiment results show that the system based on OpenSMILE toolbox can effectively obtain high-dimensional features, and its performance is excellent, which can meet most of the task requirements. It contains many kinds of acoustic feature data, which can solve the problem of over compression of the original signal, and mine the characteristics of voice waves to explain the relationship between frames. The system is higher than low dimensional features in recognizing multiple speakers. This paper designs an effective simulation system by applying speech recognition technology to multi-dimensional linguistic data analysis in the context of big data.
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