Intelligent language analysis method for multi-sensor data fusion

Abstract

Language intelligence analysis oriented to multi-sensor data fusion is of great significance for language analysis in real scenarios. On the one hand, intelligent language analysis technology can greatly improve the performance of applications such as information retrieval and machine translation, and provide technical support for semantic-level applications. On the other hand, each language has its own unique characteristics, and the advancement of the language system through language analysis technology is of great benefit to natural language analysis. In this letter, an intelligent language analysis method for multi-sensor data fusion is elaborated. Specifically, the Kalman filter algorithm is combined to perform the first preprocessing filter fusion on multi-sensor data. Then, the deep learning model is used to design a language analysis model using Bidirectional Long-Short Memory Neural Networks (Bi-LSTM) to obtain deep fusion of multi-sensor data. In the experiment, the multi-sensors are used to collect real language data and public language datasets for verification, and the results show the effectiveness of the method proposed in this letter in terms of syntactic label classification.