With the rapid development of modern society, especially the popularity of the Internet and the rapid development of computer technology, people's lifestyles are undergoing fundamental changes. The rapid development of the Internet has led to an explosion of data and how people use this data has become one of the most popular research topics in modern society. In the past, the limitations of computer technology made it difficult to manage large amounts of data effectively, but the rapid development of computer technology has now made it possible to recognise that it is possible to manage and analyse such large amounts of data. The main objective of this paper is to develop a study of in-vehicle speech TC based on a variety of machine learning algorithms. In this paper, after acquiring the features of the text, a classification model is trained and this tagged data is learned by the model to obtain a classifier. Plain Bayesian classification, nearest-neighbour classification and decision trees are introduced and their advantages and disadvantages are analysed. The results of experiments on text from in-vehicle speech devices reveal that text classification (TC) using support vector machines has good results; natural language understanding can be achieved by combining a rule-based approach by first performing classification and information extraction operations on the text; and the feasibility of architectural modifications is demonstrated through functional verification.

Fang Li. In-Vehicle Speech Text Classification based on Multiple Machine Learning Algorithms. Machine Learning Theory and Practice (2020), Vol. 1, Issue 1: 10-21. https://doi.org/10.38007/ML.2020.010102.

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