Emotion Recognition with Capsule Neural Network

作     者：Loan Trinh Van Quang H.Nguyen Thuy Dao Thi Le 

作者机构：School of Information and Communication TechnologyHanoi University of Science and TechnologyHanoi10000Vietnam Faculty of Information TechnologyUniversity of Transport and CommunicationsHanoi10000Vietnam 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第41卷第6期

页      面：1083-1098页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Emotion recognition CapsNet data augmentation mel spectrum image fundamental frequency 

摘      要：For human-machine communication to be as effective as human-tohuman communication, research on speech emotion recognition is *** the models and the classifiers used to recognize emotions, neural networks appear to be promising due to the network’s ability to learn and the diversity in configuration. Following the convolutional neural network, a capsuleneural network (CapsNet) with inputs and outputs that are not scalar quantitiesbut vectors allows the network to determine the part-whole relationships thatare specific 6 for an object. This paper performs speech emotion recognition basedon CapsNet. The corpora for speech emotion recognition have been augmented byadding white noise and changing voices. The feature parameters of the recognition system input are mel spectrum images along with the characteristics of thesound source, vocal tract and prosody. For the German emotional corpus EMODB, the average accuracy score for 4 emotions, neutral, boredom, anger and happiness, is 99.69%. For Vietnamese emotional corpus BKEmo, this score is94.23% for 4 emotions, neutral, sadness, anger and happiness. The accuracy scoreis highest when combining all the above feature parameters, and this scoreincreases significantly when combining mel spectrum images with the featuresdirectly related to the fundamental frequency.