Linguistic multidimensional perspective data simulation based on speech recognition technology and big data

Abstract

Abstract 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.