TY - GEN
T1 - Composite feature extraction for speech emotion recognition
AU - Fu, Yangzhi
AU - Yuan, Xiaochen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - This paper proposes an approach for speech emotion recognition based on the composite feature extraction. The traditional paralinguistic and prosodic features and the neurogram features are extracted and concatenated together to be the composite feature. The neural feature is presented by a computational model which outputs a series of responses of a speech's particular characteristic frequency through auditory nerve fiber. The exported responses signals are visualized as the 2D neurogram and then extracted as neural feature. With the extracted composite feature, support vector machines is used to classify the emotion. The eNTERFACE database is used and the various metrics are calculated to evaluate the performance of the proposed approach. Experimental results show that the proposed approach achieves good performances under different conditions and performs better than the related work in terms of the various evaluation metrics.
AB - This paper proposes an approach for speech emotion recognition based on the composite feature extraction. The traditional paralinguistic and prosodic features and the neurogram features are extracted and concatenated together to be the composite feature. The neural feature is presented by a computational model which outputs a series of responses of a speech's particular characteristic frequency through auditory nerve fiber. The exported responses signals are visualized as the 2D neurogram and then extracted as neural feature. With the extracted composite feature, support vector machines is used to classify the emotion. The eNTERFACE database is used and the various metrics are calculated to evaluate the performance of the proposed approach. Experimental results show that the proposed approach achieves good performances under different conditions and performs better than the related work in terms of the various evaluation metrics.
KW - Composite Feature Extraction
KW - Neurogram Features
KW - Paralinguistic and Prosodic Features
KW - Speech Emotion Classification
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85101573332&partnerID=8YFLogxK
U2 - 10.1109/CSE50738.2020.00018
DO - 10.1109/CSE50738.2020.00018
M3 - Conference contribution
AN - SCOPUS:85101573332
T3 - Proceedings - 2020 IEEE 23rd International Conference on Computational Science and Engineering, CSE 2020
SP - 72
EP - 77
BT - Proceedings - 2020 IEEE 23rd International Conference on Computational Science and Engineering, CSE 2020
A2 - Wang, Guojun
A2 - Castiglione, Aniello
A2 - Celdran, Alberto Huertas
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Computational Science and Engineering, CSE 2020
Y2 - 29 December 2020 through 1 January 2021
ER -