Abstract
A language is a valuable tool for human development and progress, and it is also an important medium for human beings to transmit information and express emotions. Language signals are ubiquitous, and it is an indispensable part of human life. This article will take the analysis of language as the starting point, combined with the relevant content of computer deep learning, and summarize various methods of language emotion recognition based on a convolutional neural network. In recent years, with the gradual intelligentization of computers, more in-depth discoveries and research have been made on language emotion research. In the deep neural network sector, most of the models used are CNN, LSTM, MO-LSTM models, and this paper aims to propose a new CLDNN (CONVOLUTIONAL, LONG SHORT-TERM) that integrates CNN, DNN, and LSTM into the same network. MEMORY, FULL CONNECTED DEEP NEURAL NETWORKS) model, compare with it, and summarize the advantages and disadvantages of CLDNN.
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