Simulation of Chinese language and text information system processing mode based on hidden Markov model

Abstract

With the popularization of computers, artificial intelligence technology has become more and more mature, among which speech recognition technology in artificial intelligence is favored by people. In the past few years, the acoustic model combined with the Gaussian mixture model and the hidden Markov model has always been in the leading position. In the field of speech recognition technology, because the development of speech data has gradually expanded, and the complexity of the data has also increased. The larger the data, the traditional data network model is slowly showing inadequacy. However, the deep neural network model is easy to deal with large and complex data modeling. This article combines the advantages of basic learning theory and speech recognition technology, and launches in-depth research on embedding learning theoretical knowledge into the field of speech recognition. Nowadays, large-scale text information databases relying on computers are becoming more and more important in linguistic research, and a large-scale corpus that fully reflects language facts and contains rich language information has been constructed. The establishment of the text information database system is long, from word segmentation, part-of-speech tagging to syntactic tagging to semantic tagging. Therefore, the characteristic of information system processing is that the systematic description depends on the application environment of understanding vocabulary and reasoning. According to different scenarios, the realization methods of semantic description roles are also different, and the description of semantic roles in correct scenarios is clearer, more detailed and systematic. Therefore, this article is of great significance for solving the semantic problem of using Chinese frame network for Chinese information processing in the context of speech recognition.