文献类型：期刊文献

英文题名：Entropy based boundary-eliminated pseudo-inverse linear discriminant for speech emotion recognition

作者：Li, Dongdong[1,2]; Sun, Linyu[1]; Wang, Zhe[1,2]; Zhang, Jing[1]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Jiangsu, 215006, China

年份：2018

卷号：11165 LNCS

起止页码：674

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20184806158311)

语种：英文

外文关键词：Speech recognition - Inverse problems

摘要：Remarkable advances have achieved in speech emotion recognition (SER) with efficient and feasible models. These studies focus on the ability of the model itself. However, they ignore the potential distributed information of speech data. Actually, emotion speech is imbalanced due to the expression of human being. To overcome the imbalanced problems of speech data, the ongoing work furthers our previous study of the Boundary-Eliminated Pseudo-Inverse Linear Discriminant (BEPILD) model through introducing the information entropy that contributes to describing the distribution of the speech data. As a result, an Entropy-based Boundary-Eliminated Pseudo-Inverse Linear Discriminant model (EBEPILD) is proposed to generate more robust hyperplanes to tackle the speech data with high class uncertainty. The experiments conducted on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database with four emotion states show that the EBEPILD has outstanding performance compared with other algorithms. ? Springer Nature Switzerland AG 2018.