Open AccessHuman speech emotion recognition via feature selection and analyzing
Author(s) -
Xiangmin Lun,
Fang Wang,
Zhi Yu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1748/4/042008
Subject(s) - speech recognition , computer science , emotion recognition , feature selection , feature (linguistics) , artificial intelligence , artificial neural network , pattern recognition (psychology) , field (mathematics) , frame (networking) , set (abstract data type) , selection (genetic algorithm) , speech processing , energy (signal processing) , mathematics , telecommunications , philosophy , linguistics , statistics , pure mathematics , programming language
Speech emotion recognition is one of the important research topics in the field of multimedia processing and human-machine interface. To obtain the most influential features of the speech data for emotion recognition, in this paper, 64 statistical features of the speech signal including short-term energy, pitch, frame, format, and spectrum energy were extracted with speech emotion database. Mean Impact Value (MIV) and the improved Correlation-based Feature Selection (CFS) were employed to select the most influential feature set. BP neural network (BPNN) was used to identify the accuracy. The proposed MIV-CFS method selected the features related to speech emotion, with less recognition error, the recognition accuracy all higher than 88%, and the highest recognition accuracy is 91.61%.