Speech emotion recognition method and system for enhancing anger and happiness recognition

Abstract

The invention provides a speech emotion recognition method and system for enhancing anger and happiness recognition. The method comprises the steps of receiving a user voice signal, and extracting acoustic feature vectors of a voice; converting the voice signal into text information, and acquiring text feature vectors of the voice; inputting the acoustic feature vectors and the text feature vectors into a speech emotion recognition model and a text emotion recognition model respectively to obtain probability values of different emotions; reducing and enhancing the obtained anger and happinessemotional probability values to obtain final emotion judgment and recognition results. The method can provide assistance for application such as emotional computing, human-computer interaction and thelike.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and emotion computing, and relates to a voice emotion recognition method and system for enhancing anger and happiness recognition. Background technique [0002] Emotion plays an important role in human intelligence, rational decision-making, social interaction, perception, memory, learning, and creation. Studies have shown that 80% of information in human communication is emotional information. In computer automatic emotion recognition, emotions are generally classified based on discrete emotion models or dimensional emotion models; in discrete emotion model classification, emotions are divided into basic emotions such as excitement, happiness, sadness, anger, surprise, and neutrality. In the classification of dimensional emotion models, in 1970 Russell believed that four quadrants were used to define the emotional space, and classified from the two dimensions of activation and valence, corr...

AI Technical Summary

Problems solved by technology

However, at present, people usually use the vector space model to describe the text vector, but if the feature items obtained by the word segmentation algorithm and the word frequency statistics method are directly used to represent each dimension in the text vector, then the dimension of the vector will be very large

Therefore, when using single text for emotion recognition, the use of text feature vectors will bring huge computational overhead to the follow-up work, making the efficiency of the entire processing process very low, and will damage the accuracy of classification and clustering algorithms, so that the obtained The result is hardly satisfactory

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