University of Oulu

S. Yan, L. Ye, S. Han, T. Han, Y. Li and E. Alasaarela, "Speech Interactive Emotion Recognition System Based on Random Forest," 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 2020, pp. 1458-1462, doi: 10.1109/IWCMC48107.2020.9148117

Speech interactive emotion recognition system based on random forest

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Author: Yan, Susu1,2; Ye, Liang1,3,4; Han, Shuai1;
Organizations: 1Department of Information and Communication Engineering, Harbin Institute of Technology, Harbin 150080, China
2School of Software and Micro Electronics, Harbin University of Science and Technology, Harbin 150080, China
3Health and Wellness Measurement research group, OPEM unit, University of Oulu, Oulu 90014, Finland
4Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security, P.R.C., Harbin 150080, China
5Electrical Engineering School, Heilongjiang University, Harbin 150080, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-11-11


In daily life, speech is the main medium of human communication, and interpersonal communication is emotional. People hope that the computer can give a response based on the emotions contained in the voice. In this paper, we build a Wechat program of speech emotion recognition system, which is based on a random forest classifier. Firstly, the system preprocesses the collected speech signals in order to reduce noise. Secondly, 16 acoustic features are extracted from the pre-processed speech signals. The system obtains the emotional features of speech by applying 12 statistical functions to the original acoustic features. The emotional classification of Berlin Speech Emotion Database uses two classifiers: the Random Forest Classifier and the Support Vector Machine. The recognition accuracy of the SVM classifier is 83%. The accuracy of the random forest classifier is 89%. Finally, the random forest classifier is used to build the speech emotion recognition system.

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Series: International Wireless Communications & Mobile Computing Conference
ISSN: 2376-6492
ISSN-L: 2376-6492
ISBN: 978-1-7281-3129-0
ISBN Print: 978-1-7281-3130-6
Pages: 1458 - 1462
Article number: 9148117
DOI: 10.1109/IWCMC48107.2020.9148117
Host publication: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Conference: IEEE International Wireless Communications and Mobile Computing Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the National Key R&D Program of China (No.2018YFC0807101). The authors would like to thank all the people who participated in the project.
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