Research on Emotion Recognition of Psychological Diagnosis Robot Based on Time Frequency Context Information Extraction
To improve the accuracy of emotion recognition in psychological diagnostic robots,a speech emotion recognition meth-od based on time-frequency contextual information is proposed.Firstly,the convolutional neural network CNN is used to extract the temporal context features of speech information;Then,the long and short term memory network LSTM is introduced to recognize con-textual emotional information features in the speech frequency domain;Finally,a fusion method of time-frequency contextual informa-tion is proposed,which integrates the extracted temporal and frequency domain contextual information features and applies them to e-motional recognition in psychological diagnostic robots.The experimental results show that compared to the speech emotion recognition results of the time-domain based context feature CFTD and frequency-domain based context feature CFFD methods,the proposed fu-sion speech emotion recognition method maintains an average emotion recognition accuracy of about 96％,with a maximum of 99.7％,which is 13.9％and 16.6％higher than the average recognition rates of the first two recognition methods,respectively.This indicates that the proposed method can effectively improve the recognition rate of temporal and frequency-domain contextual features of speech emotions.By applying this method to the backend module of the psychological diagnosis robot,accurate recognition of the robot's speech emotions can be achieved,enhancing the diagnostic effectiveness of the psychological diagnosis robot.
万方数据
维普
