Speech emotion recognition system based on L1 regularized linear regression and decision fusion

Abstract

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. © 2011 Springer-Verlag.