TY - GEN
T1 - Speech Interactive Emotion Recognition System Based on Random Forest
AU - Yan, Susu
AU - Ye, Liang
AU - Han, Shuai
AU - Han, Tian
AU - Li, Yue
AU - Alasaarela, Esko
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
KW - Machine Learning
KW - Random Forest
KW - Speech Emotion Recognition
KW - Wechat Program
UR - https://www.scopus.com/pages/publications/85089692829
U2 - 10.1109/IWCMC48107.2020.9148117
DO - 10.1109/IWCMC48107.2020.9148117
M3 - 会议稿件
AN - SCOPUS:85089692829
T3 - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
SP - 1458
EP - 1462
BT - 2020 International Wireless Communications and Mobile Computing, IWCMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Y2 - 15 June 2020 through 19 June 2020
ER -