Human-computer Interaction Speech Emotion Recognition Based on Random Forest and Convolution Feature Learning

Authors

Wang Jing, 1. College Of Optical And Electronical Information Changchun University Of Science And Technology,Changchun 130000,China; ;
Hongyan Liu, 2. Network management center of China Mobile Communication Group Jilin Co.,Ltd,Changchun 130012,China; ;
Fangfang Liu, 1. College Of Optical And Electronical Information Changchun University Of Science And Technology,Changchun 130000,China; ;
Qingqing Wang, 3. Jilin Animation Institute,Changchun 130012,China;

Abstract

Abstract: Focus on the different speech features of different types of people in the automatic speech emotion recognition of emotional robots,a random forest for speech emotion recognition is proposed,and a preliminary simulation experiment of emotional social robot system based on convolution feature learning is carried out.The results show that the emotional robot can track in real time,the seven basic emotions of excitement,anger,sadness,happiness,surprise,fear and neutrality.By using non personalized speech emotion features,the original personalized speech emotion features are supplemented,and the general emotion and special emotion are extracted.For emotional robot,using these indicators has a certain application prospect in the simulation experiment and application experiment.

Recommended Citation

Jing, Wang; Liu, Hongyan; Liu, Fangfang; and Wang, Qingqing (2020) "Human-computer Interaction Speech Emotion Recognition Based on Random Forest and Convolution Feature Learning," Journal of System Simulation: Vol. 32: Iss. 12, Article 11.
DOI: 10.16182/j.issn1004731x.joss.20-FZ0494E
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss12/11

First Page

2388

Revised Date

2020-03-28

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-FZ0494E

Last Page

2400

CLC

TP391.9

Recommended Citation

Wang Jing, Liu Hongyan, Liu Fangfang, Wang Qingqing. Human-computer Interaction Speech Emotion Recognition Based on Random Forest and Convolution Feature Learning[J]. Journal of System Simulation, 2020, 32(12): 2388-2400.

Corresponding Author

Qingqing Wang,

DOI

10.16182/j.issn1004731x.joss.20-FZ0494E