N2 - 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. L1 - http://www.journals.pan.pl/Content/101550/PDF/03_paper.pdf L2 - http://www.journals.pan.pl/Content/101550 PY - 2013 IS - No 4 EP - 470 DO - 10.2478/aoa-2013-0055 KW - speech emotion recognition KW - sparse partial least squares regression (SPLSR) KW - feature selection and dimensionality reduction A1 - Yan, Jingjie A1 - Wang, Xiaolan A1 - Gu, Weiyi A1 - Ma, LiLi PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 38 DA - 2013 T1 - Speech Emotion Recognition Based on Sparse Representation SP - 465 UR - http://www.journals.pan.pl/dlibra/publication/edition/101550 T2 - Archives of Acoustics