Archives of Acoustics, 38, 4, pp. 465–470, 2013

Speech Emotion Recognition Based on Sparse Representation

Jingjie YAN
The School of Information Science and Engineering, Southeast University
China

Xiaolan WANG

China

Weiyi GU

LiLi MA

China

Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do-
mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate
speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth.
We make use of the sparse partial least squares regression method to implement the feature selection
and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting
the SPLSR method, the component parts of those redundant and meaningless speech emotion features
are lessened to zero while those serviceable and informative speech emotion features are maintained and
selected to the following classification step. A number of tests on Berlin database reveal that the recogni-
tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality
reduction methods.
Keywords: speech emotion recognition; sparse partial least squares regression (SPLSR); feature selection and dimensionality reduction
Full Text: PDF


Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN)