Speech emotion recognition method based on speech spectrum

Abstract

The invention belongs to the field of artificial intelligence, voice processing and emotion calculation, and particularly relates to a speech emotion recognition method based on a speech spectrum. Themethod comprises the following steps of: acquiring a speech signal in real time, and converting the speech signal into a speech spectrum; and inputting the speech spectrum into a trained speech emotion recognition model to recognize speech emotion, wherein the speech emotion recognition model comprises a residual neural network based on an attention mechanism and a long short-term memory (LSTM) neural network. According to the invention, the neural network and the attention model are combined together, so that the effective features of the speech energy value in the speech spectrum can be learned more efficiently, and the accuracy of speech emotion recognition is improved.

Description

technical field [0001] The invention belongs to the fields of artificial intelligence, speech processing and emotional computing, and in particular relates to a speech emotion recognition method based on spectrograms. Background technique [0002] Speech emotion recognition is an important component of affective computing. With the development of deep learning and artificial intelligence, emotion recognition has broad application prospects in the field of human-computer interaction, and has received extensive attention from researchers. In 1995, Professor Picard of the Massachusetts Institute of Technology proposed the concept of affective computing. In 2003, the Institute of Automation of the Chinese Academy of Sciences and other units hosted the first China Affective Computing and Intelligent Interaction Academic Conference in Beijing. In 2005, the first International Affective Computing and Intelligent Interaction Academic Conference was hosted in Beijing. Experts in th...

AI Technical Summary

Problems solved by technology

[0004] At present, the combination of artificial intelligence and deep learning has made great progress in the application of speech emotion recognition. However, when using traditional neural networks and single neural networks for feature extraction and model training, due to the small data set, it is difficult to select and establish models. The factors considered are not comprehensive enough, so the extraction of speech emotional features is not comprehensive enough, which leads to the inability of the trained model to accurately classify speech emotions

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