TY - GEN
T1 - Bimodal Emotion Recognition for the Patients with Depression
AU - Wang, Xuesong
AU - Zhao, Shenghui
AU - Wang, Yingxue
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - With the rapid development of the society, over three hundred million people from worldwide suffer from depression, which has become one of the most serious health problems in the world. On the other hand, emotion recognition research works barely focuses on the depression patients although their emotion differs from that of the normal people obviously. Firstly, a speech/text emotion dataset was built up under the dialogue scenes with both the depressed patients and the normal people, and the speech/text fragments were classified into five common emotions: sadness, anger, happy, fear and neutral. Secondly, a bimodal emotion recognition algorithm is proposed, which uses a multimodal Transformer model as the feature fusion module. The experimental results show that it achieves an accuracy of 69.2% for the normal people and 59.4% for the depression patients.
AB - With the rapid development of the society, over three hundred million people from worldwide suffer from depression, which has become one of the most serious health problems in the world. On the other hand, emotion recognition research works barely focuses on the depression patients although their emotion differs from that of the normal people obviously. Firstly, a speech/text emotion dataset was built up under the dialogue scenes with both the depressed patients and the normal people, and the speech/text fragments were classified into five common emotions: sadness, anger, happy, fear and neutral. Secondly, a bimodal emotion recognition algorithm is proposed, which uses a multimodal Transformer model as the feature fusion module. The experimental results show that it achieves an accuracy of 69.2% for the normal people and 59.4% for the depression patients.
KW - Depression
KW - Emotion recognition
KW - Feature fusion
KW - Multimodal transformer
UR - http://www.scopus.com/inward/record.url?scp=85125178349&partnerID=8YFLogxK
U2 - 10.1109/ICSIP52628.2021.9688837
DO - 10.1109/ICSIP52628.2021.9688837
M3 - Conference contribution
AN - SCOPUS:85125178349
T3 - 2021 6th International Conference on Signal and Image Processing, ICSIP 2021
SP - 40
EP - 43
BT - 2021 6th International Conference on Signal and Image Processing, ICSIP 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Signal and Image Processing, ICSIP 2021
Y2 - 22 October 2021 through 24 October 2021
ER -