Speech Emotion Recognition System Based on L1 Regularized Linear Regression and Decision Fusion

被引：0

作者：

Cen, Ling ^{[1
]}

Yu, Zhu Liang ^{[2
]}

Dong, Ming Hui ^{[1
]}

机构：

[1] ASTAR, Inst Infocomm Res I2R, 1 Fusionopolis Way, Singapore 138632, Singapore

[2] South China Univ Technol, Coll Automat Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China

来源：

AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION, PT II | 2011年 / 6975卷

关键词：

feature selection; classification; linear regression; convex optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes a speech emotion recognition system that is built for Audio Sub-Challenge of Audio/Visual Emotion Challenge (AVEC 2011). In this system, feature selection is conducted via L1 regularized linear regression in which the L1 norm of regression weights is minimized to find a sparse weight vector. The features with approximately zero weights are removed to create a well-selected small feature set. A fusion scheme by combining the strength from linear regression and Extreme learning machine (EML) based feedforward neural networks (NN) is proposed for classification. The experiment results conducted on the SEMAINE database of naturalistic dialogues distributed through AVEC 2011 are presented.

引用

页码：332 / +

页数：4

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