Double sparse learning model for speech emotion recognition

Abstract

A double sparse learning (DSL) model with a pyramid structure-based feature extraction scheme to handle speech emotion recognition (SER) problem is proposed. The key novelty of the method is that the proposed DSL model is able to take into consideration two scales of the pyramid structure-based features for selecting the features which have great contributions to SER. Extensive experiments on eNTERFACE and AFEW emotion databases to evaluate the method are conducted. The experimental results demonstrate that, compared with some recent competitive methods, DSL with the pyramid structure-based feature extraction scheme has a more promising performance in dealing with the SER task.